New Word Detection and Emotional Tendency Judgment Based on Deep Structured Model

深層構造モデルに基づく新しい単語検出と感情傾向判定

With the development of social network,new words appear ceaselessly.The appearance of new word tends to characterize the social hot spot or represent certain public mood.The new word detection and emotional tendency judgment provide a new way for the public mood forecast.We constructed the deep conditional random fields model for the sequence labeling,introduced part of speech,character position,the ability of word formation as features,and combined it with the crowd sourcing network dictionary and the other third party dictionary.Traditional method based on emotional dictionary is difficult to judge the new word emotional tendency.We expressed word as a vector of K dimension based on neural network language model in order to find the nearest words to the new word in the vector space.According to the emotional tendency of these words and the distance between them and the new word,the new word sentiment is judged.The experiment on corpus of Peking university demonstrates the feasibility of the proposed model and method,in which the new word detection F-value is 0.991,and the emotion recognition accuracy is 70%.

ソーシャルネットワークの発達に伴い，新しい言葉が絶え間なく出現しています．新しい言葉の出現は，社会的なホットスポットを特定したり，特定の公共の気分を表す傾向があります．新しい単語の検出と感情的な傾向の判断は，品詞，文字位置，特徴としての単語形成の能力を導入し，それを群集調達ネットワーク辞書および他の第三者辞書と組み合わせた．感情辞書に基づく従来の方法は，新しい単語の感情的な傾向を判断することは困難である．我々は，ニューラルネットワーク言語モデルに基づいてK次元のベクトルとして単語を表現し，ベクトル空間における新しい単語に最も近い単語を見つける．これらの単語の感情的傾向およびそれらの間の距離と新しい単語，新しい単語感情が判断されます．


Variable differently implicational algorithm of fuzzy inference

To reveal the inherent essence of current differently implicational algorithms for fuzzy inference, the variable differently implicational algorithm is put forward and investigated. The differently implicational principles are improved from variable and generalized viewpoint. Furthermore, focusing on the FMP (fuzzy modus ponens) problem, unified forms of the new algorithm are obtained for R-implications and S-implications. Following that, the optimal differently implicational solutions are achieved for several specific R-implications and S-implications. Lastly, the new algorithm makes the current differently implicational algorithms compose a united whole.

ファジィ推論のための異なって暗示的なアルゴリズムの固有の本質を明らかにするために，様々な示唆的アルゴリズムが提唱され，調査されている．さまざまな暗黙の原則は，可変的かつ一般化された観点から改善される．さらに，FMP(fuzzy modus ponens)問題に焦点を当て，R-含意とS-含意のための新しいアルゴリズムの統一された形式が得られる．それに続いて，いくつかの特定のR-含意およびS-含意について，最適に異なる含意の解決策が達成される．最後に，新しいアルゴリズムは，現在の異なって暗示的なアルゴリズムを統一された全体にする．

Journal of Intelligent and Fuzzy Systems


Exploiting Opinion Distribution for Topic Recommendation in Twitter

The popular social networking service Twitter provides rapidly updated information and online trends, which enriches andbenefits peoples daily life. At the same time, how to find out the really interesting and relevant topics from the massivestreams of tweets, to provide precise topic recommendation for users, becomes a challenging problem in the real world. Previouscollaborative filtering methods give solutions to traditional recommendation tasks considering users positive reviews to helprecommend items. However, to the problem what is interesting to whom in Twitter, positive opinions toward a topic do notimply that the user will be interested in it with high probability, for the user probably prefers to know those controversial topicsor hot events with a large number of negative posts. In this paper, we exploit the characteristics of topical opinion distributionfor improving the performance of recommendation. The experimental results on a real-world Twitter dataset show that theproposed opinion-distribution-aware topic recommendation (ODA-TR) approach outperforms the state-of-the-art collaborativerecommendation methods

人気のあるソーシャルネットワーキングサービスのTwitterは，急速に更新される情報とオンライントレンドを提供し，人々の日常生活を豊かにし豊かにします．同時に，つぶやきの大衆流行から本当に興味深い関連トピックを見つけて，ユーザーに正確なトピックの推奨を提供する方法は，現実世界では挑戦的な問題になります．従来の共同フィルタリング方法は，ユーザが項目を推奨するのに肯定的なレビューを考慮して，従来の推奨タスクに解決策を与える．しかし，Twitterの誰が興味を持っているのかという問題に対して，トピックに対する肯定的な意見は，ユーザーが高い確率でそれに興味を持っているだけではなく，投稿．本稿では，オピニオンのパフォーマンスを向上させるために，オピニオン分布の特徴を利用する．現実のTwitterデータセットの実験結果は，提案された意見分配知覚トピック推奨(ODA-TR)アプローチが最先端の協力的推奨方法を上回ることを示しています．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Semi-automatic Creation of Youth Slang Corpus and Its Application to Affective Computing

This paper proposes a method to semi-automatically construct a corpus that includes Japanese youth slang called Wakamono Kotoba. The process of semi-automatic corpus construction is composed of the first step is automatic collection of example sentence, the second step is tag annotation to collected sentences, and the final step is manually modifying tag and noise reduction. In this process, there are two problems. The first problem is quality of the automatic collected corpora. The second is the accuracy of tag annotation. If the automatically annotated tags are unreliable, after all, it takes long time to modify them manually. As a solution of the first problem, we proposed a filtering method to remove meaningless sentences (noise sentences) automatically. In order to solve a second problem, we proposed an emotion estimation method that can be applied to the sentences that included youth slang and were difficult to be analyzed automatically. The result of the accuracy evaluation showed improvement in F1-Score compared to the machine learning method and confirms the effectiveness of the proposed method.

本論文では，若者言葉を含んだ発話文のコーパスを半自動構築するための手法を提案する． コーパスには，感情タグが付与されており，言葉遣いからの感情推定などのAffective Computingへの応用に用いることができる． コーパスの半自動構築のプロセスは，用例文の自動収集を行い，収集した用例文へのタグ付与，そのあと，人手によるタグ修正・ノイズ文除去作業が行われる． このプロセスにおいて，次の2つの問題がある．第1は，自動収集したコーパスの品質の問題がある．第2に，タグ付与の精度の問題である． 付与したタグが信頼できないものであると，結局，人手での修正作業に時間がかかってしまう． 第1の問題への解決策として，ノイズ文を自動で除去するフィルタリング手法を提案する． また，第2の問題への解決策として，若者言葉のような解析の難しい語が含まれていても感情推定できる手法を提案する． 提案手法の有効性を確認するために，提案したフィルタリングの精度と，提案した感情推定手法の精度評価を行った． 精度評価の結果，従来の辞書のみまたは，機械学習のみの手法と比較し，F1-Scoreの改善がみられ，提案手法の有効性が確認された．

IEEE Transactions on Affective Computing


Examining Accumulated Emotional Traits in Suicide Blogs with an Emotion Topic Model

Suicide has been a major cause of death throughout the world. Recent studies have proved a reliable connection between the emotional traits and suicide. However, detection and prevention of suicide are mostly carried out in the clinical centers, which limits the effective treatments to a restricted group of people. To assist detecting suicide risks among the public, we propose a novel method by exploring the accumulated emotional information from people's daily writings (i.e. Blogs), and examining these emotional traits which are predictive of suicidal behaviors. A complex emotion topic (CET) model is employed to detect the underlying emotions and emotion-related topics in the Blog streams, based on eight basic emotion categories and five levels of emotion intensities. Since suicide is caused through an accumulative process, we propose three accumulative emotional traits, i.e., accumulation, covariance, and transition of the consecutive Blog emotions, and employ a generalized linear regression algorithm to examine the relationship between emotional traits and suicide risk. Our experiment results suggest that the emotion transition trait turns to be more discriminative of the suicide risk, and that the combination of three traits in linear regression would generate even more discriminative predictions. A classification of the suicide and non-suicide Blog articles in our additional experiment verifies this result. Finally, we conduct a case study of the most commonly mentioned emotion-related topics in the suicidal Blogs, to further understand the association between emotions and thoughts for these authors.

最近の研究は，感情的な形質と自殺との間の信頼できる関係を証明している．しかし，自殺の検出と予防は，主に臨床センターで実施されており，効果的な治療を限られた人々の集団に限定している．一般人の自殺リスクの検出を助けるために，私たちは，人々の毎日の文章(ブログ)から蓄積された感情情報を探索し，自殺行動を予測するこれらの感情的特徴を調べることによって，新しい方法を提案する．複雑な感情トピック(CET)モデルを使用して，8つの基本感情カテゴリと5つの感情強度レベルに基づいて，ブログストリーム内の基礎感情および感情関連トピックを検出する．我々は3つの累積感情特性，すなわち連続ブログ感情の蓄積，共分散および推移を提案し，感情特性と自殺リスクとの関係を調べるために一般化線形回帰アルゴリズムを用いる．我々の実験結果は，感情遷移特性が自殺リスクをより区別する傾向にあり，線形回帰における3つの特性の組み合わせがより差別的な予測を生成することを示唆している．

IEEE Journal of Biomedical and Health Informatics


Facial expression recognition based on histogram weighted HCBP

ヒストグラム加重HCBPに基づく表情認識

In order to overcome the limitation of local binary pattern ( LBP) and its improved algorithm a facial expression method based on histogram weighted HCBP is proposed Firstly facial image is divided into some uniform sub-image and HCBP operator is used to extract texture feature Then the information entropy is used to calculate the weight of every sub-image weighted HCBP histogram of sub-image is combined with the HCBP histogram of the original image and the result histogram image is accomplished as the facial expression feature Finally the expression is classified with the nearest neighbor classifier Using the combination of Haar-like feature and CBP operator makes the description of local feature more sufficient The introduction of information entropy can distinguish the contribution of different partitions of the face The experimental results in JAFFE library and Cohn-Kanade library show that the HCBP method outperforms than existing LBP methods in both the recognition rate and the speed

局所バイナリパターン(LBP)の限界とその改良アルゴリズムを克服するために，ヒストグラム加重HCBPに基づく表情法を提案する．まず，顔画像を一定の部分画像に分割し，HCBP演算子を用いてテクスチャ特徴を抽出する．情報エントロピーを使用して，サブ画像のすべてのサブ画像重み付けHCBPヒストグラムが原画像のHCBPヒストグラムと組み合わされ，結果ヒストグラム画像が表情特徴として達成される．最後に，表現は最も近い近傍分類器Haar-like特徴とCBP演算子の組み合わせを用いることにより，局所特徴の記述がより十分になる．情報エントロピーの導入は，顔の異なる区画の寄与を区別できる．JAFFEライブラリとCohn-Kanadeライブラリの実験結果は，HCBP法は，既存のLBP法よりも認識率とsの両方において優れていることが分かった．

Journal of Electronic Measurement and Instrumentation


Overview of Speech Synthesis in Development and Methods

Spoken dialogue system is the core technology in the field of human-computer interaction, and it is an important way to realize the harmonious human-computer interaction, the research has great theory significance and application value. The advances of theory and technology in spoken dialogue systems have always been greatly concerned. The status and advances of dialogue management and spoken dialogue system are comprehensively summarized in this paper. First, the main research questions of spoken dialogue system are introduced comprehensively, including the research contents of the modules, key technologies, portability and robust design. Then, the various spoken dialogue management strategies are systematically analyzed from the perspective of theoretical models, advances and usability. Finally, several possible directions and problems for further consideration and discussion are also mentioned.

音声対話システムは，人間とコンピュータの相互作用の分野における中核技術であり，人間とコンピュータの相互作用を実現する重要な方法であり，その研究は大きな理論的意義と応用価値を持っている．音声対話システムにおける理論と技術の進歩は，常に懸念されてきた．本稿では，対話管理の現状と進化を総括的にまとめた．まず，モジュールの研究内容，主要技術，移植性，ロバストな設計など，音声対話システムの主な研究課題を包括的に紹介する．次に，理論的モデル，進歩性，ユーザビリティの観点から，様々な音声対話管理戦略を体系的に分析する．最後に，さらなる考慮および議論のためのいくつかの可能な方向および問題も言及される．


Energy-efficient Indoor Localization of Smart Hand-held Devices Using Bluetooth

Indoor localization of smart hand-held devices is essential for location based services of pervasive applications. Previous research mainly focuses on exploring wireless signal fingerprints for this purpose, and several shortcomings need to be addressed first before real-world usage, e.g., demanding a large number of APs or labor-intensive site survey. In this paper, through a systematic empirical study, we first gain in-depth understandings of Bluetooth characteristics, i.e., the impact of various factors such as distance, orientation, and obstacles on the Bluetooth RSSI (Received Signal Strength Indicator). Then, by mining from historical data, a novel localization model is built to describe the relationship between RSSI and the device location. On this basis, we present an energy- efficient indoor localization scheme that leverages user motions to iteratively shrink the search space to locate the target device. MLDB has been prototyped and evaluated in several real-world scenarios. Extensive experiments show that our algorithm is efficient in terms of localization accuracy, searching time and energy consumption.

スマートハンドヘルドデバイスの位置情報は屋内局在化サービスの普及に必要不可欠です．本論文では，体系的な実証研究を通じて，私たちは最初，BluetoothのRSSI(受信信号強度インジケータ)上の距離，方向，障害物などの様々な要因の影響のようなブルートゥース特性を深く理解します．そして，過去のデータからマイニングすることで，RSSIおよびデバイスの場所との間の関係を記述するために新たなローカリゼーションモデルは構築されています．これに基づき，我々は反復ターゲットデバイスを見つけるための探索空間を縮小するために，ユーザーの動きを活用し，エネルギー効率の良い室内ローカリゼーションスキームを提案しました．MLDBを試作し，いくつかの実世界のシナリオで評価された結果から，提案したアルゴリズムは測位精度，検索時間とエネルギー消費の点で効率的であることを示しています．


WeWatch: An Application for Watching Video Across Two Mobile Devices

In recent years, high resolution video has developed rapidly and widescreen smart devices have become popular. We present an Android application called WeWatch that enables high resolution video to be shared across two mobile devices when they are close to each other. This concept has its inspiration in machine to machine connections in the Internet of Things (loT). We ensure that the two parts of the video are the same size over both screens and are synchronous. Further, a user can play, pause, or stop the video by moving one device a certain distance from the other. We decide on appropriate distances through experimentation. We implemented WeWatch on Android operating system and then optimize Watch so battery consumption is reduced. The user ex perience provided by WeWatch was evaluated by students through a questionnaire, and the reviews indicated that WeWatch does improve the viewing experience.

近年，高解像度ビデオが急速に発展し，ワイドスクリーンのスマートデバイスが普及している． WeWatchと呼ばれるAndroidアプリケーションを述べる．これは，2つのモバイルデバイスが互いに近づいているときに高解像度のビデオを共有できるようにするものである．このコンセプトは，Internet of Things(loT)の機械間接続にインスピレーションを与える．動画の2つの部分が両方の画面で同じサイズで同期していることを確認する．さらに，ユーザは，あるデバイスをある距離だけ他のデバイスから移動させることによって，ビデオを再生，一時停止または停止することができる．我々は，実験を通じて適切な距離を決定する．AndroidオペレーティングシステムでWeWatchを実装し，ウォッチを最適化してバッテリ消費を減らした．WeWatchによって提供されたユーザーの経験は，アンケートを通じて学生によって評価され，レビューはWeWatchが視聴体験を改善することを示した．


Optimizing pre-bond and post-bond test time for three dimension IP Cores


TFSM-based dialogue management model framework for affective dialogue systems

A new dialogue management model for affective dialogue system, which aims to provide a service of information inquiry and affective interaction, is proposed in this paper. First, we construct two finite state machines (TFSM) to model the user and the system, respectively, and simulate the dialogue process as an information exchange between the two state machines. All possible state transitions in dialogue and its probabilities of the user are summarized as a user model, which is helpful for the system to inference and predict the user's internal states. Second, we further discuss the implementation methods of information inquiry and emotional response modules. Finally, we employ the return function of partially observable Markov decision processes (POMDP) model to analyze and evaluate the TFSM-based dialogue management model. The experimental results not only show the relationships between the average returns, recognition error rates, and state transition probabilities but also confirm that our TFSM-based dialogue management model outperforms the conventional FSM model.

情報照会と感情の相互作用のサービスを提供することを目的とする感情対話システムのため，新しい対話管理モデルを提案した．まず，ユーザとシステムをモデル化し，2つの有限状態機械(FSM)を構築する．2つのステートマシン間の情報交換などの対話プロセスをシミュレート．TFSMベースの対話管理モデルを分析し，評価する部分観測マルコフ決定過程(POMDP)モデルの復帰機能を使用する手法を研究した．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Precise local feature description for facial expression recognition


The Evolution of Sink Mobility Management in Wireless Sensor Networks: A Survey

Sink mobility has long been recognized as an efficient method of improving system performance in wireless sensor networks (WSNs), e.g. relieving traffic burden from a specific set of nodes. Though tremendous research efforts have been devoted to this topic during the last decades, yet little attention has been paid for the research summarization and guidance. This paper aims to fill in the blank and presents an up-to-date survey on the sink mobility issue. Its main contribution is to review mobility management schemes from an evolutionary point of view. The related schemes have been divided into four categories: uncontrollable mobility (UMM), pathrestricted mobility (PRM), location-restricted mobility (LRM) and unrestricted mobility (URM). Several representative solutions are described following the proposed taxonomy. To help readers comprehend the development flow within the category, the relationship among different solutions is outlined, with detailed descriptions as well as in-depth analysis. In this way, besides some potential extensions based on current research, we are able to identify several open issues that receive little attention or remain unexplored so far.

シンクモビリティは，無線センサネットワーク(WSN)におけるシステム性能を改善する効率的な方法として長い間認識されてきた．特定のノードのセットからトラフィックの負荷を軽減できる．このホワイトペーパーでは，シンクモビリティの問題に関する最新の調査を示す．主な貢献は，進化の観点からモビリティ管理スキームをレビューすることである．関連するスキームは，制御不可能なモビリティ(UMM)，制限付きモビリティ(PRM)，位置限定モビリティ(LRM)および無制限モビリティ(URM)の4つのカテゴリに分類されている．提案されたタクソノミーに続いていくつかの代表的な解決法が説明される．読者がカテゴリ内の開発フローを理解するのを助けるために，さまざまなソリューション間の関係を詳細な説明と詳細な分析で概説した．このようにして，現在の研究に基づく潜在的な拡張の他に，ほとんど注目を集めていない未解決の問題をいくつか特定することができた．

IEEE Communications Surveys and Tutorials


Feature-level sentiment analysis by using comparative domain corpora

Feature-level sentiment analysis (SA) is able to provide more fine-grained SA oncertain opinion targets and has a wider range of applications on E-business. Thisstudy proposes an approach based on comparative domain corpora for feature-levelSA. The proposed approach makes use of word associations for domain-specificfeature extraction. First, we assign a similarity score for each candidate feature todenote its similarity extent to a domain. Then we identify domain features based ontheir similarity scores on different comparative domain corpora. After that, dependencygrammar and a general sentiment lexicon are applied to extract and expand feature orientedopinion words. Lastly, the semantic orientation of a domain-specific feature isdetermined based on the feature-oriented opinion lexicons. In evaluation, we comparethe proposed method with several state-of-the-art methods (including unsupervised andsemi-supervised) using a standard product review test collection. The experimentalresults demonstrate the effectiveness of using comparative domain corpora.

機能レベルの感情分析(SA)は，特定の意見の目標に，よりきめ細かいSAを提供することができ，Eビジネス上のアプリケーションに広い範囲で活かせる．本研究は，機能レベルのSAの比較のためドメインコーパスに基づくアプローチを提案した．提案されたアプローチは，ドメイン固有の特徴抽出のための単語の関連付けを利用する．まず，ドメインにその類似度を示すため，各候補機能の類似性スコアを割り当てる．その後，別の比較のドメインコーパス上での類似度スコアに基づいてドメインの特徴を特定する．その後，依存文法と一般的な感情辞書を抽出し，機能指向の意見語を拡大して適用される．最後に，ドメイン固有の機能の意味向きは，フィーチャー指向意見辞書に基づいて決定される．評価では，標準的な製品レビューテストコレクションを使用して行われた．実験結果は，比較ドメインコーパスを使用することの有効性が実証された．


Optimizing the number of leaf nodes and TSVs in three dimensional scan tree

葉ノード数および3次元検査木におけるTSVsの最適化

Scan tree architecture can effectively reduce test data volume, test time and test cost for integratedcircuits. To reduce the number of leaf nodes and TSVs(through silicon vias) in scan tree for three dimensionalintegrated circuits, this paper firstly draws the conclusion that the minimum number of leaf nodes is the numberof scan cells contained in the maximal compatible group. Then, the necessary and sufficient condition achievingthe minimum number of leaf nodes is presented. On the basis above, a heuristic algorithm is proposed, whichcan minimize the number of leaf nodes and reduce consumed TSVs as many as possible. Experimental resultsdemonstrate the effectiveness of the proposed technique.

検査木アーキテクチャは，集積回路のためのテストデータの量，テスト時間およびテストのコストを効率的に減らせる．葉ノードの数と，3次元集積回路の検査木におけるTSVs(シリコン貫通電極)を削減する．本研究では，最少葉ノード数は検査セル数であると結論づけた．実験により，提案手法の有効性が確認できた．


Visualizing Emotions from Chinese Blogs by Textual Emotion Analysis and Recognition Techniques

The research on blog emotion analysis and recognition has become increasingly important in recent years. In this study, based on the Chinese blog emotion corpus (Ren-CECps), we analyze and compare blog emotion visualization from different text levels: word, sentence, and paragraph. Then, a blog emotion visualization system is designed for practical applications. Machine learning methods are applied for the implementation of blog emotion recognition at different textual levels. Based on the emotion recognition engine, the blog emotion visualization interface is designed to provide a more intuitive display of emotions in blogs, which can detect emotion for bloggers, and capture emotional change rapidly. In addition, we evaluated the performance of sentence emotion recognition by comparing five classification algorithms under different schemas, which demonstrates the effectiveness of the Complement Naive Bayes model for sentence emotion recognition. The system can recognize multi-label emotions in blogs, which provides a richer and more detailed emotion expression.

ブログの感情分析と認識の研究は，近年，ますます重要になってきている．本研究では，中国語ブログ感情コーパス(Ren-CECps)に基づき，単語，文，段落といった異なるテキストレベルからのブログ感情の可視化をおこなう．また，ブログ感情の可視化システムを実践的アプリケーションとして設計した．機械学習手法を異なるテキストレベルでのブログ感情認識の実装のための応用として用いた．感情認識エンジンに基づき，ブログ感情可視化インターフェースは，ブログにおける感情を表示する．我々は，5つの異なる枠組みに基づく分類アルゴリズムを比較し，文の感情認識の性能を評価した結果，補間ナイーブベイズモデルの文の感情認識に対する有効性を示した．

International Journal of Information Technology & Decision Making


Recognizing Sentiment of Relations between Entities in Text

Recently, sentiment analysis for identifying positive or negative opinions from texts has received much attention. In this paper,we introduce sentiment analysis into a new field, which recognizes sentiment of relations between entities in the text. Threesentiment polarities between entities are recognized, namely positive, negative, and neutral. The difficulty in this work is thatseveral pairs of entities may appear in the same sentence, and their sentiment polarities are determined by different relatedregions of the sentence. In addition, different features of words and their interactions in a related region will affect the finalsentiment. It is difficult to process this using rigid rule-based methods. Therefore, we propose a machine-learning method basedon statistics. In the proposed method, the model of conditional random fields (CRFs) is used to annotate the sentiment polaritybetween entities with the help of the syntactic dependency tree. The string of words that connects two entities in the dependencytree is used as the related region to recognize the sentiment. Experimental results and comparison with the other methods basedon different principles and related regions suggest that the proposed method shows better performance and proves its validity.Moreover, the effect of different features on the CRF is word of the i th word in the sentence. We can get a CRF model using theCRF++ tool based on feature template and training corpus. After obtaining the model, sentiment of relations could be assignedautomatically. The algorithm is shown in Fig. 2. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley &Sons, Inc.

近年，テキストからのポジティブまたはネガティブの識別のための感情分析に多くの注目が集まっている．本論文では，テキストにおけるエンティティ間の関係の感情認識という新しい分野への感情分析について紹介する．エンティティ間のいわゆるポジティブ，ネガティブ，ニュートラルの3つの感情極性が認識される．この処理においての難しさは，幾つかのエンティティ・ペアは同じ文において出現するかもしれないことや，感情極性は，文の異なる関連分野により決定されることなどがある．提案手法では，エンティティ間の感情極性の付与のために条件付き確立場を用いて，構文解析木作成に役立てる．実験結果と多手法との比較により，提案手法の有効性が証明された．


Research on facial expression recognition based on pyramid Weber local descriptor and the Dempster-Shafer theory of evidence

ピラミッド型Weber局所記述子とDempster-Shaferの証拠理論に基づく表情認識

Feature extraction is the most critical step in pattern recognition, and facial expression recognition is no exception.Weber Local Descriptor (WLD) is a Method that can effectively extract texture information from images and has the advantages of being consistent with human perception of human beings and being insensitive to noise and non-monotonic illumination variations. However, WLD has some limitations in the feature representation of local details. To overcome these limitations, a facial expression recognition Method based on Pyramid WLD (PWLD) is proposed in this study.Method First, facial images are preprocessed. This step includes the detection of faces from facial expression databases and normalization. The salient regions of segment 2 that have significant contributions to facial expression recognition from images are also preprocessed. One of these salient regions is that which includes the eyes and eye brows, while another is that with the mouth. The sizes of salient regions differ, and these regions contain different information. Thus, we stratify these salient regions and divide each layer into different blocks. The PWLD features of each block in each layer are then extracted and cascaded to represent the global and local features of a salient region reasonably, with some parameter adjustments. Second, we compute for the Chi-square distance of the PWLD histograms in both the testing and training sets. We then choose the minimum distance in every category of expressionsand normalize this distance to construct the Basic Probability Assignment (BPA) as independent evidence. To create the BPA, we use curve fitting in numerical analysis by simulating several sets of data. Finally, fusion BPA is obtained by using the Dempster-Shafer rule, and the Results are further obtained by employing thedecision-making and judgment of Dempster-Shafertheory of evidence.Result By fusing the PWLD features of the two different salient regions with Dempster-Shafer theory of evidence, we can overcome the limitations of a single regional featureand acquire more reliable and accurate Results. We conduct some cross-validation experiments on the JAFFE and Cohn-Kanade facialexpression databases, and the average recognition rates reach up to 95.81% and 97.47%, respectively. In addition, we perform some experiments with other algorithms, such as LBP, LDP, and Gabor; we also conduct some comparative experiments that combine the PWLD with different classifiers, such as 1-NN and SVM.Conclusion The WLD, which is known as a robust image descriptor, can well extract the texture information of images. Moreover, the PWLD can accurately describe the local details, which have more advantages than the WLD features. The comparative Results of some typical Methodsverify the effectiveness and fault tolerance of the proposed Method. The proposed Method has certain robustness under simultaneous noise and light conditions.

特徴抽出は，パターン認識において最も重要なステップであり，表情認識も例外ではない．本研究ではピラミッドWLD(PWLD)こに基づく表情認識手法を提案する．いくつかの典型的な方法の比較結果は，提案された方法の有効性とフォールトトレランスを確認した．提案された方法は，ノイズと光の同時の条件下で一定の堅牢性を有する．


An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Proteinprotein interactions extraction, and (2) Genesuicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on genesuicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

生物医学文献における相互作用の情報の豊富な供給は，生物医学の関連を自動抽出するためのテキスト間イニングアプローチの実装の動機づける．本論文は，生物医学の関連抽出に対応するためのパターンクラスタリングと構文解析教師なし手法を述べる．パターンクラスタリングアルゴリズムは，多項式カーネル手法に基づき，この手法は，ラベルなしデータからの作用語を識別する．半教師ありKNNアルゴリズムに基づき，提案した教師なしアプローチをパターンクラスタリング，係り受け解析とフレーズ構造解析ルールの組み合わせにより半教師アプローチに拡張した．2つの異なる評価手法を用いて評価した結果，提案した半教師ありアプローチにより，従来の半教師あり手法を上回る結果が得られた．


Unsupervised Product Feature Extraction for Feature-oriented Opinion Determination

Identifying product features from reviews is the fundamental step as well as a bottleneck in feature-level sentiment analysis. This study proposes a method of unsupervised product feature extraction for feature-oriented opinion determination. The domain-specific features are extracted by measuring the similarity distance of domain vectors. A domain vector is derived based on the association values between a feature and comparative domain corpora. A novel term similarity measure (PMITFIDF) is introduced to evaluate the association of candidate features and domain entities. The results show that our approach of feature extraction outperforms other state-of-the-art methods, and the only external resources used are comparative domain corpora. Therefore, it is generic and unsupervised. Compared with traditional pointwise mutual information (PMI), PMITFIDF showed better distinction ability. We also propose feature-oriented opinion determination based on feature-opinion pair extraction and feature-oriented opinion lexicon generation. The results demonstrate the effectiveness of our proposed method and indicate that feature-oriented opinion lexicons are superior to general opinion lexicons for feature-oriented opinion determination.

レビューからの製品素性の識別は，素性レベルの感情分析においての障壁として基本的な段階である．本研究は，素性指向意見決定のための教師なし製品素性抽出手法を提案する．ドメイン特有素性は，ドメインベクトルの類似距離を計算することで抽出される．ドメインベクトルは比較のドメインコーパスと素性との間の関連度に基づき抽出される．新たに提案した用語類似度指標PMTFIDFはドメインエンティティと候補素性の関連を評価するために導入する．実験の結果，我々の提案手法の有効性を示し，素性指向意見辞書は，一般の意見辞書より優位であることも示した．


Methodology for Developing a Nursing Administration Analysis System

Nursing administration requires a large volume of wide-ranging information, and nurse administrators are limited in their ability to compile and analyze information for nursing administration. The purpose of this study is to create methodology for developing a nursing administration analysis system to aid nurse administrators in performing outcome analysis. In this methodology, information required for nursing administration in the PSYCHOMS? (Psychiatric Outcome Management System, registered trademark) database is analyzed according to the individual needs of nurse administrators. It features a combination of a classification method and an extraction method for obtaining quantitative and qualitative data as information required for nursing administration, and enables nurse administrators to easily obtain analysis results that they directly need. This methodology converts the time required nurse administrators to collect and organize information into time for making considerations in order to devise strategies for improving the quality of nursing care services, and can improve the quality and efficiency of nursing administration. This may lead to an increase of the quality of nursing care services at psychiatric hospitals. This methodology is highly versatile as it can be applied in information management, not only for nursing, but for the entire psychiatric hospital.

看護管理に必要な情報が多岐にわたり膨大となっている．看護管理のための情報の整理や分析は看護管理者の努力だけでは限界があり，看護管理者が行うアウトカム分析を支援することを目的に看護管理分析システムを開発した．看護管理に必要な情報を看護管理者個々のニーズに応じて，PSYCHOMS○R(Psychiatric Outcome Management System, registered trademark)のデータベースに保存されているデータを分析する．その特徴は，分類と抽出の2種類の方法を混合して量的・質的データをアウトプットとして表示することができるため，看護管理に必要な情報を出力できる．看護管理者が直感的に必要と考えたことに対する分析結果を簡便に得ることができる．このシステムを活用することで，看護管理者が情報の収集と整理に要していた時間を，看護サービスの質を改善するための方略を考えるための思考の時間に変換することができるため，看護管理の質と効率性を高めることができる．その結果，精神科看護サービスの質向上につなげることが可能となる．本システムは看護だけにとどまらず，精神科病院全体の情報管理にも応用可能であり，汎用性がある．


Target Based Review Classification for Fine-Grained Sentiment Analysis

Target based sentiment classification is able to provide more fine grained sentiment analysis. In this paper, we propose a similarity based approach for this problem. Firstly, a new measure of PMI-TFIDF by combining PMI (Pointwise mutual information) and TF-IDF (term frequencyinverse document frequency) is proposed to measure the association of words for extending related features for a given target. Then Polynomial Kernel (PK) method is applied to get the similarities between a review and the related features of different targets. The sentiment orientation of a review is determined by comparing their similarities with the target based opinion words. The comparisons between PMI and PMI-TFIDF showed that the extracted features that measured by PMI-TFIDF have closer association with the targets than the extracted features measured by PMI. And the association values measured by PMI-TFIDF showed better distinction between different features. The experiments also demonstrated the effectiveness and validation of the proposed approach on target based review classification, opinion words extraction, and target based sentiment classification.

ターゲットベースの感情分析は，より粒度の細かい感情分析を実現できる．本論文では，類似度ベースのアプローチをこの問題に対して提案する．まず，PMIとTFIDFを組み合わせた新しい指標PMI-TFIDFを，単語と，関連する素性の関連付けに用いることを提案した．また，Polynomial Kernel 手法を，レビューと異なるターゲットに関連する素性における類似度を得るために適用した．評価実験の結果，PMI-TFIDFは，他の手法と比較して有効であることを示した．

International Journal of Innovative Computing, Information and Control


Automatic Role-explicit Query Extraction: A Divide-and-Conquer System Leveraging on Users' Reformulating Behaviors

This paper presents a system that can automatically extract role-explicit queries from a query log without any human intervention. The key idea underlying our system is as follows: We perform a divide-and-conquer process through differentiating the sessions in a query log as mul-sessions and sin-sessions. According to the session type, different approaches are proposed. We translate the contextual information in mul-sessions as indirect human wisdom to facilitate role-explicit query extraction on mul-sessions. Furthermore, leveraging on the role-explicit queries extracted from mul-sessions, we learn the simplified word n-gram role model (SWNR) to facilitate role-explicit query extraction on sin-sessions. The experimental results show that our proposed system is clearly favored by the indirect human wisdom hidden in mul-sessions and achieves a satisfactory performance, namely more than 79% in terms of different metrics.

本論文では，クエリ履歴からいかなる人の介入も無しで役割明瞭なクエリの自動抽出を可能にする．キーとなるアイディアは，分割と制圧処理をクエリログをmulセッションとsinセッションとした方法に基づく．セッションタイプによって，異なるアプローチが提案された．我々は，mulセッションにおいて，役割明瞭なクエリ抽出を促進するため，文脈情報を，間接的な人の知識として翻訳する．さらに，役割明瞭なクエリ抽出に，sinセッションにおいて，役割明瞭なクエリ抽出を促進するために学習された単純化単語n-gram役割モデル(SWNR)を利用する．実験結果は，提案システムがmulセッションにおいて隠れている間接的な人間の知識により有効に作用することが明らかとなり，異なる指標において，高精度な抽出を達成した．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Predicting User-topic Opinions in Twitter with Social and Topical Context

With popular microblogging services like Twitter, users are able to share opinions towards hot topics online, in a more convenient way. The user generated data in Twitter is thus regarded as an opinion-rich resource, and has been exploited for various applications. Most existing work mines opinions in Twitter by performing sentiment analysis on the text of tweets. However, a user's opinion towards a topic cannot be obtained by using those text-based approaches, when the user hasn't posted any tweet about it yet. In some cases such as launching new products or proposing new policies, the related online comments are unavailable before their release, while inferring users' opinions towards them beforehand could be potentially helpful for making correct decisions. Therefore, how to predict a user's opinions towards topics without observing the corresponding tweets from him/her, becomes a novel problem presenting both challenges and opportunities. We focus on the problem of predicting user-topic opinions, and propose a framework mph{ScTcMF} for incorporating social and topical context to solve it. The experimental results on a real-world Twitter dataset show that our framework outperforms the state-of-the-art collaborative filtering methods, and demonstrate that both social context and topical context are effective in improving the prediction performance.

Twitterをはじめとするマイクロブログサービスが普及してきたことで，ホットトピックに対する意見の共有が可能となった．ユーザはTwitterにおいて豊富な意見のリソースとしてTwitterを生成する．そして，多様な応用に利用できる．我々は，ユーザのトピックの意見を予測するという問題に着目し，ScTcMFフレームワークを提案する．実世界のTwitterデータセットの本提案手法の実験結果により，最先端の協調フィルタリング手法を実現し，ソーシャル文脈とトピック文脈が予測精度の改善に効果的であることを示した．

IEEE Transactions on Affective Computing


Single Parameter Logarithmic Image Processing For Edge detection

Considering the non-linear properties of the human visual system, many non-linear operators and models have been developed, particularly the logarithmic image processing (LIP) model proposed by Jourlin and Pinoli, which has been proved to be physically justified in several laws of the human visual system and has been successfully applied in image processing areas. Recently, several modifications based on this logarithmic mathematical framework have been presented, such as parameterized logarithmic image processing (PLIP), pseudo-logarithmic image processing, homomorphic logarithmic image processing. In this paper, a new single parameter logarithmic model for image processing with an adaptive parameter-based Sobel edge detection algorithm is presented. On the basis of analyzing the distributive law, the subtractive law, and the isomorphic property of the PLIP model, the five parameters in PLIP are replaced by a single parameter to ensure the completeness of the model and physical constancy with the nature of an image, and then an adaptive parameter-based Sobel edge detection algorithm is proposed. By using an image noise estimation method to evaluate the noise level of image, the adaptive parameter in the single parameter LIP model is calculated based on the noise level and grayscale value of a corresponding image area, followed by the single-parameter LIP-based Sobel operation to overcome the noise-sensitive problem of classical LIP-based Sobel edge detection methods, especially in the dark area of an image, while retaining edge sensitivity. Compared with the classical LIP and PLIP model, the given single parameter LIP achieves satisfactory results in noise suppression and edge accuracy.

本論文では，適応パラメータベースのソーベルエッジ検出アルゴリズムと画像処理のための新たな単一パラメータ対数モデルを提案する．さまざまな実験を行って，評価を行った．提案した手法の有効性を確かめることができた．

IEICE Transactions on Information and Systems


Universal Triple I Method for Fuzzy Reasoning and Fuzzy Controller

As a generalization of the triple I method, the universal triple Imethod is investigated from the viewpoints of both fuzzy reasoningand fuzzy controller. The universal triple I principle is putforward, which improves the previous triple I principle. Then,unified form of universal triple I method is established based onthe (0,1)-implication or R-implication. Moreover, the reversibilityproperty of universal triple I method is analyzed from expansion,reduction and other type operators, which demonstrate that itsreversibility property seems fine, especially for the case employingthe (0,1)-implication. Lastly, we analyze the response ability offuzzy controllers based on universal triple I method, then thepracticability of triple I method is improved.


Speech Emotion Recognition Using a Novel Feature Set

Speech emotion recognition has attracted attentions from increased number of researchers in Psychology,Computer science,Phonetics and related disciplines.This paper discusses combining multiple features forspeech emotion recognition to yield improved performance. It proposes using combination of multiplefeatures to improve recognition performance. Using ChineseLDC emotional database, the experimentresults show that a novel feature set of MFCC, fundamental frequency, MFCC, MFCC, energy,zero-crossing ratio and amplitude can get the best performance as 95% and 84% for emotional speechrecognition of four emotional state and six emotional state.

Journal of Computational Information Systems


Hybrid Approach for Word Emotion Recognition

In recent years, with plenty of online resources constantly emerging, emotion recognition in text has become increasingly important in human-computer interaction. Word emotion plays a very important role in emotion analysis of sentences or documents. This paper proposes a hybrid approach to recognize word emotion in the dimension of eight emotion categories with corresponding intensities based on the Chinese emotion corpus. First, we present a new algorithm of semantic similarity computation for aiding emotion intensity computation, and design a new algorithm of emotion vector computation by making use of both morpheme characteristics and semantic relations. And then, we adopt Support Vector Machines (SVM) model for the secondary classification to the words whose emotions can not be calculated by the semantic analysis algorithm. Our approach achieves the accuracy of 54.00% and 78.75% for exact match and all five types of hit respectively on the basis of the core emotion lexicon CL4. Experimental results show that the integration of morpheme characteristics and semantic relations can improve the classification accuracy efficiently.

テキストの感情認識は人間とコンピュータのインタラクションにおいて重要な役割に立つ．ワードの感情はテクいストの感情認識に極めて重要である．本論文で，中国語感情コーパスに基づいて対応する強度を有する8感情カテゴリでワード感情を認識するためのハイブリッドアプローチを提案する．実験結果は，形態素特性と意味関係の統合を効率的に分類精度を向上させることができることを示している．


Creating Chinese-English Comparable Corpora

Comparable Corpora are valuable resources for many NLP applications, and extensive research has been done on information mining based on comparable corpora in recent years. While there are not enough large-scale available public comparable corpora at present, this paper presents a bi-directional CLIR-based method for creating comparable corpora from two independent news collections in different languages. The original Chinese document collections and English documents collections are crawled from XinHuaNet respectively and formatted in a consistent manner. For each document from the two collections, the best query keywords are extracted to represent the essential content of the document, and then the keywords are translated into the language of the other collection. The translated queries are run against the collection in the same language to pick up the candidate documents in the other language and candidates are aligned based on their publication dates and the similarity scores. Results show that our approach significantly outperforms previous approaches to the construction of Chinese-English comparable corpora.

コンパラブルコーパスは，多くの自然言語処理アプリケーションにとって価値ある資源であり，近年のコンパラブルコーパスに基づく情報マイニングにおける研究は幅広くおこなわれている．しかし，十分に大きなサイズの公開されて利用できるコンパラブルコーパスは，現在のところ存在しない．本論文では，2つの独立したニュース記事コレクションからコンパラブルコーパスを作成するための双方向CLIRベースの手法について述べる．オリジナル中国語文書コレクションと英語文書コレクションを，XinHuaNetから独立にクロールし，一貫した方法においてフォーマットする．2つのコレクションからのそれぞれの文書に対し，文書の内容の要点を再現する最善のクエリキーワードを抽出し，それらのキーワードはもう一方のコレクションの言語に翻訳される．同じ言語におけるコレクションに，翻訳されたクエリが出現すると，もう一方の言語の文書の候補抽出のために，公開日時と類似スコアに基づきアラインメントされる．本手法の評価の結果，中英コンパラブルコーパスの構築において，従来手法よりも明らかに優れていることが示された．


The new growing research eld of Affective Computing (AC) provides a newhorizon for quantitative analysis of human emotional states using IT techniques. In thispaper, a new academic system called nriching Mental Engineering (EME)" is proposedfor the problem of mental health from the view of engineering. EME is being establishedas an academic discipline, by being keenly aware of the poverty of the mind from whichpeople living in modern society suffer. In EME, quantitative measurement of richness ofthe mind is regarded as a central technique. This is measured from the information likesubject physiological data, textual information, behavior, and tone of voice. Meanwhile,EME also systemizes external stimuli by an emotional energy function. The emotionenergy function is proposed to calculate a person's emotional stimuli at a certain pointfrom factors like choice of words, voice, facial expressions, physiological information, andbehavior. Furthermore, an application of EME is illustrated through an analysis of thedepressive tendencies in blogs.

近年の感情コンピューティングにおける分野では，人間の感情状態を，情報処理技術を用いて定量的に分析する手法が提案されている． 本研究では，豊心工学という新しい学問体系を確立し，その技術により，精神の健康の問題を解決することを目的とする．本論文では，豊心工学における分析手法の一例として，ブログにおけるうつ状態を分析するための手法を取り上げる．

International Journal of Innovative Computing, Information and Control


EMS: Efficient Mobile Sink Scheduling in Wireless Sensor Networks

Sink scheduling, in the form of scheduling multiple sinks among the available sink sites to relieve the level of traffic burden, is shown to be a promising scheme in wireless sensor networks (WSNs). However, the problem of maximizing the network lifetime via sink scheduling remains quite a challenge since routing issues are tightly coupled. Previous approaches on this topic either suffer from poor performance due to a lack of joint considerations, or are based on relaxed constraints. Therefore, in this paper, we aim to fill in the research blanks. First, we develop a novel notation Placement Pattern (PP) to bound time-varying routes with the placement of sinks. This bounding technique transforms the problem from time domain into pattern domain, and thus, significantly decreases the problem complexity. Then, we formulate this optimization in a pattern-based way and create an efficient Column Generation (CG) based approach to solve it. Simulations not only demonstrate the efficiency of the proposed algorithm but also substantiate the importance of sink mobility for energy-constrained WSNs.

シンクスケジューリングとはトラフィック負荷のレベルを軽減するために利用可能なシンクサイト間で複数のシンクをスケジューリングする形で，無線センサネットワーク(WSN)で有望であることが示されている．本稿では，まず，シンクの配置とバウンド時変ルートに配置パターン(PP)を開発する．その後，パターンベースの方法で，この最適化を策定し，効率的な列の生成(CG)，それを解決するためのベースのアプローチを作成する．シミュレーションは提案アルゴリズムの有効性を実証する．


A New Modified Centroid Classifier Approach for Automatic Text Classification

To enhance the automatic text classification task, this paper proposes a novel approach for treating the problem of inductive bias incurred by the centroid classifier assumption. This approach is a trainable classifier, which takes into account tfidf as a text feature. The main goal of the proposed approach is to take advantage of the most similar training errors in the classification model for successively updating that model based on a certain threshold. The proposed approach is practical and flexible to implement. The complete performance of the proposed approach is measured at several threshold values on the Reuters-21578 text categorization collection. Experimental results show that the proposed approach can improve the performance of the centroid classifier better than traditional approaches (traditional centroid classifier, support vector machines, decision trees, Bayes nets, and N Bayes) by 1, 1.2, 4.1, 7.5, and 11%, respectively.

テキストの自動分類タスクを強化するために，本稿では重心分類器の仮定によって生じた誘導性バイアスの問題を処理するための新しいアプローチを提案している．このアプローチは，テキスト特徴としてアカウントTFIDFにかかる訓練可能な分類器である．提案されたアプローチでは，連続的に一定の閾値に基づいて，そのモデルを更新するための分類モデルの中で最も類似した訓練誤差を利用する．実験結果は，提案されたアプローチは従来のアプローチよりも優れ重心分類器の性能を向上させることが分かった．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Employing Hierarchical Bayesian Networks in Simple and Complex Emotion Topic Analysis

Traditional emotion models, when tagging single emotions in documents, often ignore the fact that most documents convey complex human emotions. In this paper, we join emotion analysis with topic models to find complex emotions in documents, as well as the intensity of the emotions, and study how the document emotions vary with topics. Hierarchical Bayesian networks are employed to generate the latent topic variables and emotion variables. On average, our model on single emotion classification outperforms the traditional supervised machine learning models such as SVM and Naive Bayes. The other model on the complex emotion classification also achieves promising results. We thoroughly analyze the impact of vocabulary quality and topic quantity to emotion and intensity prediction in our experiments. The distribution of topics such as Friend and Job are found to be sensitive to the documents' emotions, which we call emotion topic variation in this paper. This reveals the deeper relationship between topics and emotions.

従来の感情モデルは，ドキュメント全体に対して単一の感情を識別していた．これは，ほとんどのドキュメントでは複数の感情が混ざって表現されている事実を無視していることになる．そこで本論文では，ドキュメントに複雑な感情を見つけるために感情の強度とトピック・モデルを感情分析に導入し，ドキュメントが表わす感情がトピックに応じてどのように変化していくかについて分析した．潜在的なトピックと感情の関係を表現するために，階層的ベイズネットワークを用いた．実験を行ったところ，提案した手法は単一の感情分類に対して従来の手法より優れた結果を得た．更に複雑な感情分類に対しても有効であることが確認された．得られた結果を詳細に分析したところ，ドキュメント中のトピックの分布が表現している感情と密接な関係があることがわかった．


A New Hybrid Model for Automatic Text Classification

This paper introduces three novel pointes the first one is creating the proposed tfsc,dfsc algorithm to sort the terms in N-gram model which effected goodly on the classification performance. The second one is proposing a new distance similarity method for N-gram model where the new method solves the problem of the difference in representation lengths among classes and documents. The third one is establishing a hybrid Center Profile Vector (CPV) classification model based on the modified N-gram and centroid classifier models. The hybrid (CPV) classification model gain a higher classification accuarcy beteer than N-gram and centroid models as the paper will show in the evaluation result.

本論文は，N-gramモデルにおける用語のソートのためのtfsc, dfsc アルゴリズムを提案し，N-gramモデルのための新しい距離類似度尺度を文書のクラスごとの長さの違いの問題を解消するために提案した．また，N-gramと重心分類モデルに基づくハイブリッドCPV分類モデルを確立した．評価の結果，ハイブリッドCPV分類モデルは，N-gram重心モデルよりも高い正解率を示した．

The Online Journal on Computer Science and Information Technology, OJCSIT


Class-indexing-based term weighting for automatic text classification

Most of the previous studies related on different term weighting emphasize on the document- indexing-based and four fundamental information elements-based approaches to address automatic text classification (ATC). In this study, we introduce class-indexingbased term-weighting approaches and judge their effects in high-dimensional and comparatively low-dimensional vector space over the TF.IDF and five other different term weighting approaches that are considered as the baseline approaches. First, we implement a class-indexing-based TF.IDF.ICF observational term weighting approach in which the inverse class frequency (ICF) is incorporated. In the experiment, we investigate the effects of TF.IDF.ICF over the Reuters-21578, 20 Newsgroups, and RCV1-v2 datasets as benchmark collections, which provide positive discrimination on rare terms in the vector space and biased against frequent terms in the text classification (TC) task. Therefore, we revised the ICF function and implemented a new inverse class space density frequency (ICSdF), and generated the TF.IDF.ICSdF method that provides a positive discrimination on infrequent and frequent terms. We present detailed evaluation of each category for the three datasets with term weighting approaches. The experimental results show that the proposed class-indexing-based TF.IDF.ICSdF term weighting approach is promising over the compared well-known baseline term weighting approaches.

本研究では，クラス索引ベースの用語重み付けアプローチを提案し，高次元かつ，比較的低次元のベクトル空間において，TFIDFと5つの異なる用語重み付けアプローチよりも有効であることを判断する．まず，クラス索引付ベースのTF.IDF.ICFによる用語重み付けアプローチは，転置クラス頻度(ICF)を組み込んだ．実験では，TF.IDF.ICFが，Reuters-21578の 20のニュースグループと，RCV1-v2データセットをベンチマークセットとして，テキスト分類タスクにおけるベクトル空間中の希少な用語と高頻度な用語に対するバイアスにおける正例の識別を提供する．提案手法は，3つのデータセットに対するそれぞれのカテゴリの詳細な評価をおこない，既存の用語重み付けのベースライン手法と比較して良好な結果を得た．


Finding Emotional Focus for Emotion Recognition at Sentence Level

Emotion recognition at sentence level is one of the fundamental problems of textual emotion understanding. Based on the observation that sentence emotional focus can be expressed by some clauses in this sentence, this paper proposes to find the emotional focus for sentence emotion recognition. For the sake of breaking through the problems brought about by depending on emotion lexicons, we first recognize word emotions in a sentence based on Maximum entropy model. And then homogeneous Markov model is built for clause emotion recognition; After that, a strategy based on emotion selection is proposed for a sentence with multiple clauses, and genetic algorithm is used for clause selection by textual feature weighting. The experimental results show that, comparing with the baseline, there are 9.1% and 3.6% improvement respectively for two different evaluations. It is demonstrated that finding emotional focus by clause selection is able to improve the performance of sentence emotion recognition significantly.

文レベルの感情認識は，テキスト感情理解における重要な問題である．観察に基づき，文の感情焦点が，いくつかの節により表現できる．本論文では，文の感情認識のための感情焦点の検出手法を提案する．提案手法の評価の結果，異なる2つの評価において，提案手法は，ベースライン手法と比較し，9.1%，3.6%の改善がみられた．


Use of a Dialogue System to Retrieve the Memories of Elderly Individuals with Dementia and Determine the Physiologically Effective Evaluation Indicators

International Journal of Advanced Intelligence (IJAI)


Required Function of the Caring Robot with Dialogue Ability for Patients with Dementia

AIA International Advanced Information Institute


A Novel Estimation Method of Onomatopoeic Word's Feeling based on Mora Sequence Patterns and Felling Vectors

モーラ系列と音象徴ベクトルによるオノマトペの印象推定法

Onomatopoeic words are frequently used for expression of rich presence. These words can be understood easily for native speakers. Therefore most of onomatopoetic words are not written in a national language dictionary, or only a part of meaning is described. On the other hand, it is hard to understand a meaning of onomatopoetic words for non-native speakers. They can neither feel a meaning of an onomatopoetic word nor look it up in a dictionary. In this paper, an estimation method of feeling of an onomatopoeic word has been proposed. The feeling of the onomatopoeic word is inferred by using several features, such as morae sequence pattern of a onomatopoeic word, feeling of each mora, and so on. From the experimental results, the estimation performance of the proposed method was 0.345 (F-value). It was approximately 80% of the estimation performance given by human (F-value was 0.427). It can be said that the proposed method is useful for supporting learners of onomatopoeic words.

オノマトペとは，擬音語や擬態語の総称である．文章で物事を表現する際に，より印象深く，豊かで臨場感のあるものにするために利用される．このようなオノマトペによる表現は，その言語を母語としている人であれば非常に容易に理解することができるため，国語辞書などにあえて記載されることは稀なケースである．また，記載があったとしても，使用されているオノマトペをすべて網羅して記載していることはない．そのため，その言語を母語としない人にとっては学習し難い言語表現である．そこで本稿では，オノマトペが表現する印象を推定する手法を提案する．日本語を対象に，オノマトペを構成する文字の種類やパターン，音的な特徴などを手がかりに，そのオノマトペが表現している印象を自動推定する．これにより，日本語を母語としない人に対して，日本語で表現されたオノマトペの理解の支援に繋がると考えられる．結果として，オノマトペの表記内のモーラ系列間の類似度とオノマトペの表記全体の音象徴ベクトルによる類似度を用いた手法が最も良い推定結果となり，参考値である人間同士の一致率の8割程度にまで近づくことができた．


Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writers perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

ウェブログは，人類のコミュニケーションの方法に変化をもたらせた．ブログ・ポストのテキストから感情を推定する方法の開発は，テキストの読み上げやコンピュータによる各種支援機能といった多くのアプリケーションにとって益々重要となる．本論文では，ウェブログの文章を対象としたの感情自動識別を提案する．この方法はマルチラベル・テキスト・カテゴリー分類タスクとしてモデル化されている．8次元の感情タグが付与された感情コーパスRen-CECpsから12273のウェブログ文を用いて感情識別実験を行った．メイン特徴と付加特徴を導入し，幾つかの新しいモデルを提案したところ，よりよりよい性能持つことが実験的に確認された．

IEEJ Transactions on Electronics, Information and Systems


Linguistic-Based Emotion Analysis and Recognition for Measuring Consumer Satisfaction - An Application of Affective computing

A growing body of research suggests that affective computing has many valuable applications in enterprise systems research and e-businesses. This paper explores affective computing techniques for a vital subarea in enterprise systems consumer satisfaction measurement. We propose a linguistic-based emotion analysis and recognition method for measuring consumer satisfaction. Using an annotated emotion corpus (Ren-CECps), we first present a general evaluation of customer satisfaction by comparing the linguistic characteristics of emotional expressions of positive and negative attitudes. The associations in four negative emotions are further investigated. After that, we build a fine-grained emotion recognition system based on machine learning algorithms for measuring customer satisfaction; it can detect and recognize multiple emotions using customers' words or comments. The results indicate that blended emotion recognition is able to gain rich feedback data from customers, which can provide more appropriate follow-up for customer relationship management.

感情コンピューティングは様々なシステムやe・ビジネスに有効である可能性を持っている．本論文では，企業においてきわめて重要なエリア---顧客満足度調査---に感情コンピューティング技術を用いる方法を提案する．言語解析による感情認識法を用い，商品に対する顧客の評価文章から顧客満足度を計測する．まず感情的な表現を手掛りに全体としての満足度を評価し，その後機械学習を用いて各文や単語ごとに詳細な感情識別を行う．その結果，ひとつの評価文章に含まれる複数の感情等を詳細に識別することが可能となる．実験の結果から，こうした複数の感情を正しく識別することで，より正確に顧客満足度を推定し，結果としてより的確なフィードバックを返せることがわかった．


Differently implicational -universal triple I restriction method of (1, 2, 2) type

From the viewpoints of both fuzzy system and fuzzy reasoning, a new fuzzy reasoning method which contains the -triple I restriction method as its particular case is proposed. The previous -triple I restriction principles are improved, and then the optimal restriction solutions of this new method are achieved, especially for seven familiar implications. As its special case, the corresponding results of -triple I restriction method are obtained and improved. Lastly, it is found by examples that this new method is more reasonable than the -triple I restriction method.

本論文では，ファジー・システムとファジー推論の両方の観点から，α-トリプルI制限法を特殊なケースとして含む新しいファジー推論方法を提案する．この方法では，従来のα-トリプルI制限法を改善し，最適な制限法，特に7つのよく知られている推論結果を得ることができた．提案法の特殊なケースとして，α-トリプルI制限法に対応する結果が得られ，この方法がα-トリプル制限法より性能のよい方法であることが実験的に確認された．

Journal of Systems Engineering and Electronics


A Novel Fast Fractal Image Coding Algorithm Based on Texture Feature

A novel, fast fractal image coding algorithm based on the texture feature is proposed in this paper. Fractal image coding isa very promising technique for image compression. However, it has not been widely used because of the long encoding timeand high computational complexity. The most fractal image encoding time is spent in determining the approximate D-blockfrom a large D-blocks library by using the global searching method. Clustering the D-blocks library is an effective method toreduce the encoding time. First, all the D-blocks are clustered into several parts based on the new texture feature derived fromvariation function; second, for each R-block, the approximate D-blocks are searched for in the same part. In the search process,we import control parameter ; this step avoids losing the most approximate D-block for each R-block. Finally, the R-blockswhose least errors are larger than the threshold given in advance are coded by the quad tree method. We have performed asimulation with MATLABR2010a to verify the effectiveness of the proposed algorithm. The experimental results show that theproposed algorithm can be over 6 times faster than the moment-feature-based fractal image algorithm; in addition, the proposedalgorithm also improves the quality of the decoded image and increases the PSNRs average value by 2 dB. The comparisonsdemonstrate that this method is better than the fractal image coding algorithm based on statistical features

本研究では，テクスチャ特徴量に基づくフラクタル画像符号化アルゴリズムを提案する．フラクタル画像符号化手法は，画像圧縮のための期待される技術である．しかし，符号化のための時間がとても長いことと，計算量が複雑であるため，あまり広く用いられていない．多くのフラクタル画像符号化では，大局的な検索手法を用いることによって大きなD-ブロックライブラリから近似のD-ブロックにを決定する際において時間がかかる．D-blockライブラリをクラスタリングすることで，符号化にかかる時間を削減するための効率的な手法を提案する．MATLABを用いたシミュレーション実験をおこない，提案手法により，重心特徴量ベースのフラクタル画像アルゴリズムよりも，高速に符号化できることが示せた．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


An unequal clustering algorithm based on energy balance for wireless sensor networks

Sensor nodes in sensor networks often gather data and deliver them to the sink. However, the energy and radio range constraints force them to cooperate in transmitting the data to the destination with multihop communication. To achieve this, nodes have to be clustered and some of them are elected as the cluster head nodes to transmit the aggregated data. Unfortunately, the clustering brings extra traffic load to the cluster head nodes, particularly those closer to the sink. These nodes die faster than before as their energy is drained at a high rate. Therefore, some clustering and cluster head election algorithms were suggested to use the unequal clustering size to extend the lifetime of cluster head nodes. However, it is difficult to obtain the accurate cluster size in the process of unequal clustering. Moreover, some cluster head nodes with the overfull cluster members may die early. We propose an enhanced unequal clustering size algorithm to optimize the unequal cluster size in the different layers and balance the dissipation energy of all cluster head nodes. Simulation results show that our algorithm dissipates approximately the same energy of cluster head nodes in the different layers per round and obviously increases the number of rounds of data gathering and transmission.

センサネットワークにおけるセンサノードは，多くの場合，データを収集し，配信する．本論文では，異なる層における不均等なクラスタサイズを最適化し，すべてのクラスタヘッドノードの消費エネルギーのバランスをとるように拡張不等クラスタサイズのアルゴリズムを提案する．シミュレーション結果は，提案したアルゴリズムの有効性を示した．


Semi-Automatic Complex Emotion Categorization and Ontology Construction from Chinese Knowledge

In order to recognize one's intention from the communication, both the meaning and the emotion are necessary to be interpreted correctly. But until now the study of fine-grained theory of emotion is still full of challenges. This paper analy-zes emotion category according to the statistics of Affective Word (AW) hierarchy and describes an e-motion ontology from Chinese knowledge resource semi-automatically created for human machine in-teraction. The emotion hierarchy is called complex emotion. Firstly, over 7 000 AWs have been anno-tated and their detailed explanations had been col-lected for an affective lexicon and then the con-sistent relationships are automatically parsed and a serial of emotion hierarchical structures are built up. More than 50 affective categories are extracted by a lexical clustering algorithm and about 5 000 nouns and adjectives and 2 000 verbs are categorized into the predicate hierarchy. The results have been evalu-ated to be valid by two metrics.


Emotional Vector Distance Based Sentiment Analysis of Wakamono Kotoba

In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applies Earth Mover's Distance(EMD) to vector of words. As a result ofthe evaluation experiment using 14,440 sentences, higher estimation accuracy is obtained by considering emotional distance between words - an approach that had not been used in the conventional research - than by using only word importance value.

本研究では，日本語俗語(若者言葉)に感情を付与した辞書を自動構築するため，コーパス内において若者言葉と共起する語(主に感情表現)の感情極性ベクトルを用いて，Earth Mover's Distanceによる感情ベクトルを推定する手法を提案する．感情表現への感情極性ベクトルは，感情表現辞書と感情極性対応表を組み合わせることにより付与できる．本手法の評価実験をおこない，単語重要度を用いた従来手法よりも高精度な感情ベクトルが推定できることを明らかにした．


Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

In this paper, we provide a Word Emotion Topic (WET) model to predict the complex word e-motion information from text, and discover the dis-tribution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories: joy, love, expectation, surprise, anxiety, sorrow, anger and hate. We use a hierarchical Bayesian network to model the emo-tions and topics in the text. Both the complex emo-tions and topics are drawn from raw texts, without considering any complicated language features. Our experiment shows promising results of word emo-tion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text. We also explore the topic distribution by ex-amining the emotion topic variation in an emotion topic diagram.


Hyponym Extraction from the Web by Bootstrapping

This paper proposes an effective method to automatically extract hyponym from the Web for Chinese. The method extractshyponyms for a given hypernym through weak supervision in two stages: the first stage is submitting a hypernym and a seedhyponym as a query to Web search engine, and automatically extracting hyponyms matching with a Chinese doubly anchoredhyponymy pattern from the Web by bootstrapping. In order to reduce noise data in bootstrapping extraction, we propose a set offiltering rules to ensure matching of the proper hypernym in the extracted sentence. The second stage is ranking all the extractedcandidate hyponyms by an integrated ranking algorithm which takes into account measures both of linkage frequency betweencoordinate hyponyms and of semantic similarity between the hypernym and candidate hyponym based on co-occurrence statistics.

本論文では，自動的にWebから下位語を抽出するための効果的な方法を提案する．ブートストラップ抽出にノイズデータを低減するために，抽出された文に適切な上位概念を一致するかどうか確認アルゴリズムを提案した．実験などを通じて提案手法の有効性を評価した．


Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition

In this study, we make a scheme to explore the importance of modification relation for the emotional keywords annotation and emotion types recognition. We extract three modification features which are degree words,negative words and conjunctions from the Chinese emotion corpus named Ren-CECps. Beside word and part-of speech,three modification relations are adopted as feature in this study. We have carried out eight experiments with different feature sets for emotional keywords annotation and emotion types recognition in sentence level. Eight basic emotion types have been selected and Conditional Random Fields have been employed as the algorithm. In the part of evaluation, we demonstrate the importance of the modification features and our experiment results show the effectiveness of the modification features for improving the performance of emotional keywords annotation and emotion types recognition.

本研究では感情キーワードの付与と，感情タイプ認識における，修飾関係の重要度を推定する手法を提案した．具体的には，中国語感情コーパスRen-CECpsを用いて，程度を表す語，否定語，接続語の3つの修飾素性を抽出する．実験の結果，文に対する感情キーワードの付与と，感情タイプの認識の精度の改善を達成できた．

International Journal of Intelligent Engineering and Systems


Reversibility of FMT-Universal Triple I Method Based on IL Operator

For the FMT-universal triple I method based on L I operator, its reversibility is investigated. Aiming at thecase that the second operator employs L I , the reversibility of FMT-universal triple I method is analyzed, where the firstoperator respectively takes seven different implication operators. It is found that the FMT-universal triple I methodseems excellent from the viewpoint of reversibility, and the second operator prefers to take L I in the universal triple Imethod.

American Journal of Engineering and Technology Research


Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts

Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.

パターン認識の従来研究では，より良い認識結果を得る分類器のアンサンブルアプローチが示された．本研究は，CoNLL-2010の，生物医学テキストにおける境界の手がかりと領域の検出といったタスクのための投票技術を適用した．6つの機械学習ベースのシステムを，3つの異なる投票手法により組み合わせた．評価実験により，CoNLL-2010評価データセットにおいて，我々のシステムが，境界検出タスクでF-Score 87.49%，領域検出タスクで60.8%の結果を得ることができ，従来手法よりも明らかに有効であることが分かった．

IEICE Transactions on Information and Systems


Estimation of word emotions based on part of speech and positional information

Recently, studies on emotion recognition technology have been conducted in the fields of natural language processing, speech signal processing, image data processing, and brain wave analysis, with the goal of letting the computer understand ambiguous information such as emotion or sensibility. This paper statistically studies the features of Japanese and English emotional expressions based on an emotion annotated parallel corpus and proposes a method to estimate emotion of the emotional expressions in the sentence. The proposed method identifies the words or phrases with emotion, which we call emotional expressions, and estimates the emotion category of the emotional expressions by focusing on the three kinds of features: part of speech of emotional expression, position of emotional expression, and part of speech of the previous/next morpheme of the target emotional expression.

近年，自然言語処理，音声信号処理，画像処理，脳波分析などの分野において感情認識技術の研究がおこなわれている．本研究では，日英対訳文パラレルコーパスに感情タグを付与した，日英対訳感情コーパスを対象として，単語への感情付与をおこなうことを目的とする．本手法において，確率的モデルを用いて系列ラべリングをする際に，従来の品詞情報だけでなく，位置情報を用いることで，より高精度な単語感情推定がおこなえることが，評価実験の結果明らかとなった．


Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation

Recently, there is a lot of research that aims to estimate emotion from text. The meaningsof linguistic expressions used in daily life vary depending on the context in which theyare used. That is to say, the information they contain presents ambiguities. Especiallythe so-called Wakamono Kotoba, Japanese language used by young people containssemantic ambiguities. Such words are usually not included in the existing dictionaries,making the meanings of these words difficult to be recognized. In this research projectwe proposed a method to estimate emotion from sentences that include Wakamono Kotobaby using statistical learning methods such as Na¨ıve Bayes method and Accumulationmethod. The existing research usually focused on learning methods using word or wordN-gram as features. However, such word-based features are insufficient to process WakamonoKotoba because Wakamono Kotoba often cannot be recognized as one semanticword by morphological analysis. In this paper we describe how we constructed the linguisticresource of Wakamono Kotoba emotion corpus to be used for emotion recognitionand introduce the features we obtained from statistical analysis. Our Wakamono Kotobaemotion corpus includes Japanese words used by young people to express emotion. Thesewere mainly gathered from Weblogs that were written by young people from their teensto their twenties.

本研究では，若者言葉感情コーパスを構築し，その分析をおこなう．そして，感情推定への応用について述べる．従来，発話文に感情タグを付与したコーパスは数あれど，文中に含まれる未知語(とくに若者言葉)に着目した研究はほとんどなかった．この理由として，基本的に若者言葉が辞書に載らない語として，軽視されてきたことがある．しかし，Web上のテキストデータが容易に入手できるようになった現在，Web上の話し言葉に近い口語文をリソースとしてテキストマイニングなどの研究をおこなう場面が確実に増加してきている．本研究で提案する若者言葉感情コーパスは，若者言葉を含んだ発話文をブログなどに含まれる発話文から抽出し，手作業により感情タグを付与していくことにより構築する．この作業自体は，人手による判断が重要であり，手作業で行う必要があるため，今後は自動構築手法についても研究していく必要がある．

International Journal of Advanced Intelligence (IJAI)


Chinese New Word Identification: A Latent Discriminative Model with Global Features

Chinese new words are particularly problematic in Chinese natural language processing. With the fast development of Internet and information explosion, it is impossible to get a complete system lexicon for applications in Chinese natural language processing, as new words out of dictionaries are always being created. The procedure of new words identification and POS tagging are usually separated and the features of lexical information cannot be fully used. A latent discriminative model, which combines the strengths of Latent Dynamic Conditional Random Field (LDCRF) and semi-CRF, is proposed to detect new words together with their POS synchronously regardless of the types of new words from Chinese text without being pre-segmented. Unlike semi-CRF, in proposed latent discriminative model, LDCRF is applied to generate candidate entities, which accelerates the training speed and decreases the computational cost. The complexity of proposed hidden semi-CRF could be further adjusted by tuning the number of hidden variables and the number of candidate entities from the Nbest outputs of LDCRF model. A new-word-generating framework is proposed for model training and testing, under which the definitions and distributions of new words conform to the ones in real text. The global feature called Global Fragment Features for new word identification is adopted. We tested our model on the corpus from SIGHAN-6. Experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags with satisfactory results. The proposed model performs competitively with the state-of-the-art models.

中国語新しい単語の同定については，自然言語処理における重要な課題の一つであるが，多くの問題が残されている．本論文では，新しい単語の識別のため，グローバルフラグメントと呼ばれるグローバル機能をもつ潜在的識別モデルを活かした．実験により，提案したモデルの有効性を確かめることができた．

Journal of Computer Science and Technology


Recognition of Word Emotion State in Sentences

Emotional word spotting has been an important step for the problems of textual emotion recognition and automatic emotionlexicon construction. To express and recognize emotion of words, especially for the words bear undirect emotions, emotionambiguity, or multiple emotions, the notion of word emotion state is proposed, which describes the state of combined basicemotions in a word. On the basis of Ren-CECps (an annotated emotion corpus) and MaxEnt (maximum entropy) modeling,we explore the effectiveness of several features and their combinations for word emotion recognition in certain contexts. Acomparative study on the performances of word emotion and word emotion state recognition is given. The experimental resultsshowed that a model using word emotion state can greatly outperform using word emotion.

感情語の検出は，言語感情認識と自動感情辞書構築における問題のための重要なステップである．単語の感情認識および表現，とくに，直接的でない感情を表現する単語，また，曖昧に表現された感情や，複数の感情，単語感情状態の心象を提案し，それらは，単語において基本感情の組み合わせにより表現される状態である．Ren-CECpsコーパスと，最大エントロピーモデルに基づき，いくつかの特徴と，ある文脈においてそれらを組み合わせることによる有効性を確認した．単語感情を用いるよりも，単語感情状態を用いた方が高い性能が得られることを評価実験の結果により明らかにした．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


An Improvement of Example-based Emotion Estimation Using Similarity between Sentence and each Corpus

コーパスごとの類似度を考慮した用例に基づく感情推定手法の改善

発話文を感情ごとに分類したコーパスを構築し，入力文と最も類似度が高い発話文を含むコーパスの感情を推定結果として出力する用例ベースの感情推定手法が提案されている．従来手法ではコーパスを構築する際，発話テキストの収集者が個人個人で発話文の分類先を決定しているため，分類先を決定する基準が個々によってぶれてしまう．これにより，例えば``希望''のコーパスの中に喜びの発話文が混じるといったことが起こり，推定成功率を下げてしまう．本稿ではこの問題を解決するため，コーパスごとにおける入力文の形態素列の出現回数を用いて，入力文とコーパスの類似度を定義する．そしてこの類似度を従来手法に導入した新たな類似度計算式を提案する．これにより，誤って分類されてしまった発話文の影響を緩和することができる．評価実験では従来手法と比べて成功率が 21.5 ポイント向上し，提案手法の有効性が確認できた．


From Cloud Computing to Language Engineering, Affective Computing and Advanced Intelligence

This paper discusses the definition, intension, and extension of language engineering, affective computing, and advanced intelligence, as well as the relationship among the three fields. By reporting the latest progress and future prospects, we attempt to unify language engineering and affective computing with the concept of advanced intelligence.``Cloud computing'' has recently become a very popular topic. Instead of discussing the concept and intension of cloud computing, this paper focuses on how progress in language engineering, including natural language processing and natural language understanding, will enormously aid in the achievement of cloud computing. It particularly deals with how to construct clouds, how to sweep clouds, and how to predict and exploit clouds.Another concept discussed in this paper is ``affective computing''. To a large extent, this is a breakthrough in advanced intelligence. Here, it refers to a high fusion of natural and artificial intelligence, and depends on the emotional capacity entrusted to the computer, including the capability of affective recognition and affective generation.

本論文では，言語工学，感性工学，先進的知性の定義，目的，展望，そして3つの分野の関係性について考察する．また，最新の進捗や将来の見通しから，言語工学と感性工学を先進的知性というコンセプトでひとつに結びつけることを試みる．クラウドコンピューティングの概念や目的を議論するのではなく，言語工学(自然言語処理及び自然言語理解を含む)の進歩が，クラウドコンピューティングの成果にどのように大きく貢献できるかに焦点をあてる． 本論文では，クラウドの構築法，削除法，予測・利用法を中心に，感性工学についても議論する．また，クラウドコンピューティングは，感情認識及び感情創生等，コンピュータに委ねられた感情的な能力による，自然と人工知能の高度な融合という意味で，先進的知能においても飛躍的な進歩である．


Sentence Emotion Analysis and Recognition Based on Emotion Words Using Ren-CECps

Emotion recognition on text has wide applications. In this study, we make an analysis onsentence emotion based on emotion words using Ren-CECps (a Chinese emotion corpus).Some classification methods (including C4.5 decision tree, SVM, NaiveBayes, ZEROR,and DecisionTable) have been compared. Then a supervised machine learning method(Polynomial kernel method) is proposed to recognize the eight basic emotions (Expect,Joy, Love, Surprise, Anxiety, Sorrow, Angry and Hate). Using Ren-CECps, we get theemotion lexicons for the eight basic emotions. Polynomial kernel (PK) method is usedto compute the similarities between sentences and the eight emotion lexicons. Then theexperiential knowledge derived from Ren-CECps is used to recognize whether the eightemotion categories are present in a sentence. The experiments showed promising results.

テキストからの感情認識は幅広い分野にアプリケーションが期待できる．本研究では，Ren-CECps(中国語感情コーパス)を用いて，感情語に基づく文感情の分析を行う．C4.5決定木，SVM，ナイーブベイズ，ZEROR，決定表等の分類手法を比較し，さらに，8種類の感情(期待，喜び，愛，驚き，不安，悲しみ，怒り，憎しみ)認識のために，教師あり機械学習法(多項式カーネル法)を提案した．Ren-CECpsを用いて，8種類の基本感情毎に感情辞書を作成し，多項式カーネル(PK)法を使って，文と8種類の感情辞書の間の類似度を比較した．そして，Ren-CECpsから導出した知見を用いて，8種類の感情カテゴリが文に表出されているかどうかを認識した．実験結果は大変期待できるものだった．


Detecting New Words from Chinese Text Using Latent Semi-CRF Models

Chinese new words and their part-of-speech (POS) are particularly problematic in Chinese natural language processing. With the fast development of internet and information technology, it is impossible to get a complete system dictionary for Chinese natural language processing, as new words out of the basic system dictionary are always being created. A latent semi-CRF model, which combines the strengths of LDCRF (Latent-Dynamic Conditional Random Field) and semi-CRF, is proposed to detect the new words together with their POS synchronously regardless of the types of the new words from the Chinese text without being pre-segmented. Unlike the original semi-CRF, the LDCRF is applied to generate the candidate entities for training and testing the latent semi-CRF, which accelerates the training speed and decreases the computation cost. The complexity of the latent semi-CRF could be further adjusted by tuning the number of hidden variables in LDCRF and the number of the candidate entities from the Nbest outputs of the LDCRF. A new-words-generating framework is proposed for model training and testing, under which the definitions and distributions of the new words conform to the ones existing in real text. Specific features called ``Global Fragment Information'' for new word detection and POS tagging are adopted in the model training and testing. The experimental results show that the proposed method is capable of detecting even low frequency new words together with their POS tags. The proposed model is found to be performing competitively with the state-of-the-art models presented.

インターネットと情報技術の速い開発に従って，新しい単語が自然言語処理において大きな問題となっている．本論文では，新しい単語の検出及び形態素解析のため，潜在的半条件付き確率場(Conditional Random Fields: CRF)モデルが提案された．その特徴としては，LDCRF(潜在的ダイナミックなCRFと半CRFの強さを組み合わせることによって，複雑な前処理が回避されることができた．よって，トレーニング速度を加速し，計算コストを減少させる．実験により，提案されたモデルの有効性が確認された．

IEICE Transactions on Information and Systems


Sentimental Classification based on Kernel Methods and Domain Semantic Orientation Dictionaries

Kernel-based algorithms exploit the document information encoded in the inner-product between all pairs of document data items, avoiding explicitly the computation of the feature vector for a given input, therefore it gets considerable attention in classification tasks. In this paper, we focus our attention on the problem of sentimental classification based on three kernel methods: latent semantic kernel (LSK), polynomial kernel (PK), and Gaussian kernel (GK). It is well known that LSK has good performance in text classification, but it has relative low efficiency because of the process of the SVD decomposition, especially runs on large corpora. Our experiments demonstrate that PK has higher precision and efficiency compared with LSK and GK for the problem of sentimental classification. In particular, we compared the performances on different semantic orientation dictionaries, and found that the domain semantic orientation dictionaries could enhance the performance greatly. Also, our method could categorize the reviews with different degrees, such as 5-star, 4-star and 1-star by sorting the similarities between the reviews and the semantic orientation dictionaries. In our method, tagged corpus and certain rules are not necessary, so it is practical and has high efficiency.

International Journal of Innovative Computing, Information and Control


A Practical Sightseeing Question Answering System Based on Integrated Knowledge-Base

In this paper, a restricted domain question answering (QA) system is described. This research presents a practical sightseeing question answering system based on integrated knowledge-base. First, we use hand-crafted corpus and online resources as knowledge-base, then perform question understanding based on sightseeing place word detection and question classification. We exploit different answer extraction strategies while based on various knowledge-base (hand-crafted corpus or online resources) for answer retrieval and generation. Experimental results show the proposed method is effective for improving our former models.

本研究では，統合的知識ベースに基づいた実用的な観光案内のための質疑応答システムを提案する．まず，我々は手作業で作成したコーパスとオンラインリソースを知識ベースとして用い，観光地用語の検出と質問分類に基づいた質問の理解を行う．回答の検索と生成には，様々な知識ベース(手作業で作成したコーパスやオンラインリソース)に基づきながら，異なる回答を抽出する手法をとった．実験の結果，提案手法は既存モデルの改善に効果的であることがわかった．

IEEJ Transactions on Electronics, Information and Systems


Human Emotion Model Based on Discourse Sentence for Expression Generation of Conversation Agent

There was a conversation agent on the generation method of facial expression. It is necessary for the conversation system like the human for communication. In the previous method, at first a word which could influence the feeling was defined. Facial expression was changed according to the word which influences the feeling in discourse. Whereas, facial expression could not be changed if there was not a word that was defined in the discourse. Hence, we proposed a human emotion model for the expression generation of the conversation agent. The method based on the human emotion model can solve problem of the previous method and may make a more humanity conversation agent. In this study, we put a human emotion tag to discourse of talks scenarios and model to conversation agent of human emotion. There were two kinds of methods that put the human emotion tag to discourse. We make the human emotion model by scenarios that adopts the human emotion tag. Used the human emotion model to create facial expression of conversation agent. The assessment experiment was performed by using the systems of previous method and two human emotion models, and compared the results between the three methods


An ERP Research on Chinese Japanese Learners' Processing of Japanese Kanji and Sentences

This paper investigates the recognition process of Japanese Kanji and sentences for Chinese Japanese learners (CJL) and native Japanese speakers (NJS), by analyzing the event-related potential (ERP) differences between the two groups while they visually recognized Japanese Kanji and sentences The results showed that no significant differences were found between the two groups while they recognized Japanese Kanji, but significant differences were found in Japanese sentences condition, which demonstrated that the neural mechanisms of recognition process of Japanese sentences including Kana between the two groups were not identical The N400 latency of NJS appeared earlier, reflecting that NJS recognized Japanese sentences quicker than CJL did In contrast, the obvious longer N400 duration of CJL illuminated it was more difficult for CJL to recognize Japanese sentences When recognizing ambiguous sentences, CJL P600 only appeared over the right prefrontal cortex and lasted longer than that of NJS, reflecting that syntactic integration and revision of ambiguous sentences for CJL was related with the right hemisphere, and the processing load was more difficult than that of NJS. The results showed that, for CJL, the difficulty of Japanese language learning was the recognition and understanding of Japanese sentences with Kana, not Japanese Kanji


Exploiting Lexical Information for Function Tag Labeling

This paper proposes a novel approach to annotate function tags for unparsedtext. What distinguishes our work from previous attempts is that we assign function tagsdirectly basing on lexical information other than on parsed trees, thus our method isgeneral and easily portable to languages in shortage of parsing resources. In order todemonstrate the effectiveness and versatility of our method, we investigate function tagassignment for unparsed Chinese text by applying two statistical models, one is log-linearmaximum entropy model, another is maximum margin based support vector machinemodel. We show that function tag types could be determined via powerful lexical featuresand effective learning algorithms. Currently, our method achieves the best F-score of86.4 when tested on the Penn Chinese Treebank data, the highest score ever reported forChinese text.


Spokesperson Detection Method for Autonomous Robot in Complex Communication Environment, Based on Image Processing

Fro providing a man-machine communication system, the computer or the robot may be enabled to understand and simulate the command from human. As a pre-requisite, the system should be enabled to recognize the spokesperson, who is speaking to it However, in the realistic scene, the noise from the complex environment may cause some difficulties for the system to realize this function By imitating the performance of human, we try to use the eyes of the robot to solve the problem In this research, we propose a new spokesperson detection method based on image processing The system might find and identified all the possible spokesperson around it, by detecting and recognizing the faces in the video form the cameras on the robot It might also find out the spokesperson who is speaking to it, by judging the mouth action of possible spokesperson Using eye cameras and an omni-directional camera, we tried to realize the system and some experiments were conducted to verify the new method


A blog emotion corpus for emotional expression analysis in Chinese

Weblogs are increasingly popular modes of communication and they are frequently used as mediums for emotional expression in the ever changing online world. This work uses blogs as object and data source for Chinese emotional expression analysis. First, a textual emotional expression space model is described, and based on this model, a relatively fine-grained annotation scheme is proposed for manual annotation of an emotion corpus. In document and paragraph levels, emotion category, emotion intensity, topic word and topic sentence are annotated. In sentence level, emotion category, emotion intensity, emotional keyword and phrase, degree word, negative word, conjunction, rhetoric, punctuation, objective or subjective, and emotion polarity are annotated. Then, using this corpus, we explore these linguistic expressions that indicate emotion in Chinese, and present a detailed data analysis on them, involving mixed emotions, independent emotion, emotion transfer, and analysis on words and rhetorics for emotional expression.

ウェブログは，ますます人気の通信モードであり，頻繁に変化し続けるオンラインの世界で感情的な表現のための媒体として使用されている．この論文は，中国語感情解析のためのデータソースとしてブログを使用している．まず，テキストの感情表現の空間モデルを提案した．文書や段落のレベル，感情カテゴリ，感情の強さでは，話題語とトピックセンテンスには注釈をつけてある．文レベル，感情カテゴリ，感情の強さ，感情的なキーワードやフレーズ，単語，否定的な言葉，句読点，客観的または主観的，感情極性などをラベル付けた．


Learning Relation Instances for Chinese Domain Ontology from the Web

This paper presents a method to extract relation instances from the Internet in order to acquire knowledge that has some relations for domain ontology. We propose an ontology relation instance learning model: data sources are collected though the Web search engine and the extracted instances are constructed in Web ontology language (OWL) by Protege in Chinese. Basically, the extraction of relation instances contains syntactic patterns for filtering concepts and relevance measurement for selection of relation instances. A relevance measurement based on co-occurrence statistics is presented in this paper, which measures the semantic similarity of the measure between candidate instances and predefined domain keywords using Web search engines. In the experiment, we extract festival customs for different festival instances using relation has_custom between festival class and custom class in the Chinese festival ontology, and prove the effectiveness of our method.

本稿では，ドメインオントロジーにおけるいくつかの関係を持って知識を獲得するために，インターネットから関係インスタンスを抽出する方法を提案した．データソースはウェブ検索エンジンにより収集されされたインスタンスである．基本的には，関係インスタンスの抽出は，関係インスタンスを選択するための概念と関連性の測定をフィルタリングするための構文パターンが含まれている．共起の統計情報に基づいて関連性の測定は，Web検索エンジンを用いて候補インスタンスと事前に定義されたドメインキーワード間の措置の意味的類似性を測定する．実験では，中国のお祭りのオントロジーで祭りクラスとカスタムクラスとの関係を使用して別の祭りインスタンスの祭りの習慣を抽出し，本手法の有効性を確認した．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Create Special Domain News Collections through Summarization and Classification

In this paper, we present a novel technique to create a special domain news collection system from really simple syndication (RSS) news sites through summarization and classification. The main aim of this research is to build a self-sufficient news collection system in disaster domain. In this news collection system, we used new strategies and algorithms to mine news from RSS sites, recognized and collected disaster news using automatic summarization and classification. The most striking dissimilarity between our study and previous work is that we use a novel summary approach to improve the classification performance. This paper discusses the effect of summarization and classification model on system performance. Results show that our method yields a better performance in this field.

本論文では，要約と分類を活かしたニュースサイトからの特別なドメインのニュース収集システムの作成手法を提案した．その主な目的は，災害ドメインにおけるニュース収集システムを構築することである．このニュース収集システムでは，RSSサイトからニュースマイニングに新しい戦略とアルゴリズムを使用し認識し，自動要約と分類を活かして，災害ニュースを集めた．システムのパフォーマンス上の要約と分類モデルの効果について実験を行った．その結果により提案手法の有効性を確認した．


An extraction technique of place-related words based on an association mechanism

We are conducting research on the development of an intelligent robot that can converse naturally with people. Humans manipulate smooth communications by retrieving, understanding and judging several common sense concepts from conversations consciously or unconsciously. In this paper, we focus on the expression of the place from such common sense concepts. We propose a technique which is able to associate the person and thing existing in the place and the event done on the place from the word expressing the place based on an association mechanism. F-measure of the place subject and place object was 88.0% and 84.3% respectively. The average F-measure of both was 86.2%. Moreover, result of proposed technique was 37.7% better than result of a traditional technique which used case frame dictionary. These results show that proposed technique was effective and the place judgement system has achieved a judgement similar to human's sense.

本論文では常識的概念の中でも場所表現に焦点をあてる．人物とその場所に存在する物，そしてその場所で起こった出来事を，連想メカニズムに基づいて，場所を表現する言葉から関連づける技術を提案する．場所主体語と場所目的語のF値は88.0%, 84.3%，両者のF値平均は86.2%であった．さらに，提案手法を用いた結果は37.7%で格フレーム辞書を用いた既存手法より良かった．これらの結果から，提案手法は効果的で，場所判断システムは人の感覚に近い判断をしたことがわかった．

International Journal of Knowledge Engineering and Soft Data Paradigms


Chinese POS Tagging Using Restricted Maximum Entropy Model

This paper presents Chinese Part-of-speech (POS) tagging using maximum entropy technique, in which we introduce a novel gain-driven method for feature selec-tion, then we describe the restricted training method for model learning. We test our approach on the simplified Chinese corpus of Peking University China and achieve an accuracy of 97.80% and 98.60% over fine and coarse grained tag set -a significant improvement over the existing Chi-nese POS tagger.


Chinese New Word Detection and POS Tagging Based on DUCRF

In Chinese language processing, new words are particularly problematic. It is impossible to get a complete dictionary as new words call always be created. We proposed a unified Dual-chain unequal-state CRF model to detect new words together with their part-of-speech in Chinese texts regardless of the word types such as compound words, abbreviation, person names, etc. The Dual-chain Unequal-state CRF model has two stale chains with unequal number of states. The unequal state chains could model flexible hierarchical lexical information for both Chinese new word detection and POS tagging, and also integrate complex context features like the global information. The experimental results show that the proposed method is capable of detecting even low frequency new words and their parts-of-speech synchronously with satisfactory results.

Information : an International Interdisciplinary Journal


Question Classification for Chinese Cuisine Question Answering System

This paper presents an integrated method on question classification for Chinese cuisine question answering (QA) system. First, we exploit the domain knowledge to enrich question preprocessing, then classification features are extracted by means of domain attributes and the rule-based classifier is constructed. Support vector machine (SVM) classifier is used for secondary classification to the questions which cannot be matched with rules. A prototype system based on the proposed method has been constructed and an experiment on 453 natural language questions collected from Internet has been carried out. It achieved an accuracy of 96.22%. Result shows that a small number of linguistically motivated domain features can efficiently classify questions of Chinese cuisine QA system.

本論文では，中国語料理質問応答(QA)システムのための質問の分類上の統合法を提案する．分類機能は，ドメインの属性の手段とルールベースの分類器を構築することにより，抽出された質問の前処理を行い，豊かにするドメイン知識を活用する．サポートベクターマシン(SVM)分類器は，ルールに一致させることができない質問に二分類のために使用される．提案された方法に基づいたプロトタイプシステムを構築しており，インターネットから収集した453自然言語質問に対する実験が行われていた．それは，96.22パーセントの精度を達成した．


New Similarity Scale to Measure the Difference in Like Patterns with Noise

A new similarity scale called the Geometric Distance, that numerically evaluates the degree of likeness between two patterns is proposed. Traditionally, the similarity scales known as the Euclidean distance and cosine similarity have been widely used to measure likeness. Traditional methods do not perform well in the presence of noise or pattern distortions. In this paper, a new mathematical model for a similarity scale is proposed which overcomes these limitations of the earlier models, while improving the overall recognition accuracy. Experiments in speech vowel recognition were carried out under various SNR levels in a variety of noisy environments. In all cases a significant improvementin recognition accuracy is demonstrated, with the improvement most pronounced in the noisiest conditions. In fact, at a SNR of 5 dB in a subway, the recognition accuracyimproved from 65% to 75% and at 20 dB SNR from 98.4% to 99.6% over the MFCC method. Numerical modeling of simple patterns is used to demonstrate the principles behind the Geometric Distance.

類似性の度合いを数値的に評価するために幾何学的尺度と呼ぶ新しい類似性尺度を提案する．通常，類似性の尺度はユークリッド距離やコサイン類似度が使用されているが，ノイズや歪みの存在する場合には，上手く機能しない．本論文は，それらの欠点を克服する類似性尺度の新しい数学的モデルを提案し，認識精度が改善できた．様々なノイズが含まれる母音認識で実験を行い，全ての場合でかなりの改善効果が見られ，MFCC法よりも優れていることが判った．

International Journal of Advanced Intelligence (IJAI)


Learning to Classify Semantic Orientation on On-line Document

As a result of advance in Internet technology, automatic text sentimentclassification for a large amount of on-line documents in the form of surveys or calledreviews becomes attractive. The task of sentiment classification is to construct an effectiveclassifier with the knowledge data of vocabularies semantic meaning and the relationshipsbetween the vocabularies to determine the sentiment orientation of a document. In thispaper, one method combining HowNet knowledge base with a robust supervised sentimentclassifier is proposed. It computes semantic similarity of characteristic words and phrasesby using HowNet. Sentiment features of text are divided into characteristic words andphrases, and they adopt the positive and negative terms as features of sentiment classifier.Finally in the experiment, evaluation results show the effectiveness of our method.

International Journal of Innovative Computing, Information and Control


A New Approach to Data Clustering Using a Computational Visual Attention Model

Cluster analysis plays an important role in many respects such as knowledge discovery, data mining and information retrieval. In this paper, we propose a newapproach inspired by the early vision system of the primate for data clustering. Humanbeings are able to locate key points that contains more important information in a complex scene. To realize this function, our approach uses a computational visual attentionmodel that selects and extracts salient areas in visual field by local difference features.Then the extracted salient areas in original visual field can be regarded as the clusters inthe data feature space. Without prior knowledge, this attention model based approach canidentify data clusters with arbitrary shapes at different scales. Finally our algorithm hasbeen tested in the evaluation experiments on the benchmark datasets to show its competitive performance.