Using Machine Learning to Classify Information Related to Child Rearing of Infants from Twitter

Advances in Machine Learning & Artificial Intelligence


System to Correct Toxic Expression with BERT and to Determine the Effect of the Attention Value

This paper describes an extended experiment on a system that converts sentences with toxic expressions into safe sentences, along with the evaluation and influence of the system. In recent years, toxicity on social media has created many problems. We evaluated the effectiveness of the proposed system for identifying toxic sentences using a prediction model based on Bidirectional Encoder Representations from Transformers (BERT) and then converting them into safe sentences using attention values and a score that indicates whether the sentences are appropriate after the predictive conversion. Six patterns of methods were tested, with Pattern 6 being the most effective for mitigating toxicity. This pattern is a technique that changes the way to take the top sentences of a beam search for each number of treatments, in addition to converting words with an attention value above a threshold and their adjacent words and phrases and words registered in the toxic dictionary. We used multiple indicators to judge the effectiveness of this method and evaluated its ability to make the text safe while preserving its meaning.

Communications in Computer and Information Science


Determination of individual factors associated with hallux valgus using SVM-RFE

Introduction This cross-sectional study aimed to determine the factors related to hallux valgus (HV) and their importance using support vector machine-recursive feature elimination (SVM-RFE). Methods A total of 864 participants aged 18 years were enrolled. The Manchester scale was used to determine the presence of HV (summed scores for both feet 4). The questionnaire included items such as age, sex, height, weight, and foot measurements. These internal factors were analyzed to determine if they are related to HV using SVM-RFE. Results The results of tenfold cross-validation using SVM-RFE revealed that the numbers of feature selections were 10, 10, and 9 for age, sex, and body weight, respectively, and these factors were shown to be related to HV. HV was found to be more common in women than in men (women, 24.9%; men, 7.6%), but the sex difference was not significant in older people. Conclusion Age and sex were found to be important factors associated with HV identified via feature selection using SVM-RFE.


Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts

This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral.

本論文では，SNS上の投稿テキストや添付画像などのマルチモーダル情報を対象に，投稿内容から感情情報を分析し，バズや流行の発生のレベルを予測する手法を提案する．提案手法は，事前予測に用いる場合は投稿時の情報のみを用い，事後分析に用いる場合は時間誤差の情報を用いて，様々な角度から拡散スケールを分析することが可能である．具体的には，事前に学習させた汎用言語モデルBERTを用いてツイートと返信ツイートをベクトル化し，添付画像を学習させた画像認識用ニューラルネットワークモデルを用いて特徴ベクトルに変換する．また，投稿内容の感情情報を解析するために，独自の感情解析モデルを用いて，ツイート，返信ツイート，画像特徴から感情を推定し，感情特徴として入力に追加する．評価実験の結果，ツイートに言語特徴(BERTベクトル)と画像特徴を付加した提案手法は，単一特徴のみを用いた手法と比較して高い性能を達成することができた．感情特徴量の効果を明確に観測することはできなかったが，考察及び分析の結果，ツイートとその返信の一致する感情が多いほど，共感行動が発生し，いいねやRTの値が大きくなる傾向があり，結果的にツイートがバイラル化する可能性が高くなると考えられる．


Extraction of Lifestyle Habits Described in Blogs of Diabetics

糖尿病患者のブログに記述された生活習慣の抽出

Tackling diabetes, an increasingly common lifestyle-related illness, requires information on patients lifestyle and habits. Blogs maintained by patients afflicted with the incurable illness may be useful for analyzing how lifestyle affects health-status. In this paper, we propose an intrinsic expression extractor that extracts keywords related to lifestyle and health-status from blogs of patients diagnosed with type-2 diabetes. To counter class imbalance and add to the corpus, the proposed method tags each keyword with information based on cue-words extracted from manually tagged data. The named-entity recognition (NER) for the extracted keywords uses a bidirectional gated recurrent unit neural network (BiGRU) and was evaluated for accuracy by cross-validation. We obtained F1-score of approximately 0.76. Although the accuracy of extraction can further be improved, the novel approach has applications in analyzing and improving the lifestyle of diabetes-afflicted patients.

本研究は難病患者の闘病活動における生活習慣の維持や管理を支援するシステムを構築するための一歩として，糖尿病患者の生活習慣(食事，睡眠，運動など)や病状(血圧，体調など)に関連したキーワードの抽出を目指す．糖尿病の闘病患者のブログデータを収集し，そこから双方向ゲート付きリカレントユニット(GRU)を用いて生活習慣や病状に関連したキーワードの自動抽出を行う．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Development of an AI-based Dental Support System for Panoramic X-ray Images

AIを用いたパノラマX線画像からのカルテ入力支援システムの開発

Research and development of AI-based diagnostic systems in the medical and dental fields is flourishing worldwide. However, there are few practical dental support systems and dental databases. Support for medical record input is needed to reduce the burden at diagnosis. We have been developing a dental database with detailed annotation information and building an AI system for automatic teeth detection, teeth numbering, teeth contour estimation, and disease diagnosis from panoramic X-ray images since 2019. Three dentists and one expert of dental radiology created our database, which includes teeth number based on FDI method, coordinates of teeth contour, and various dental conditions, using Anotee, a software developed for creating dental databases. Our system consists of multiple deep neural networks that were trained using 1,781 panoramic X-ray images and annotations, which excluded deciduous teeth and rare dental conditions. The deep neural network to classify dental conditions is based on EfficientNetV2-S and can diagnose multiple dental conditions. To verify our systems usefulness, we evaluated two dental condition classifications for 20 dental conditions and 10 dental conditions such as caries, periodontitis, root canal filling, inlay, composite resin, crown, pontic, implant, and impacted tooth. We performed 5-fold cross validation and calculated precision, sensitivity, and specificity. Experimental results were encouraging. For the diagnosis of 20 conditions, precision was 90.4%, sensitivity was 86.1%, and specificity was 99.4%; for the diagnosis of 10 conditions, precision was 92.9%, sensitivity was 90.0%, and specificity was 99.1%. The system achieved high accuracy, suggesting that AI systems are useful in assisting medical record input support.

我々は従来の診断支援に関する技術をカルテ入力支援に応用するために，詳細なアノテーションを付与した歯科データベースの構築と，パノラマ X 線画像を対象としたカルテ入力支援システムの研究開発を行ってきた．本システムは，歯検出ニューラルネットワーク，セグメンテーションニューラルネットワーク，状態診断ニューラルネットワークを用いて，パノラマ X 線画像から，歯の検出，歯式推定，歯の輪郭推定および歯の状態診断を行うことができる．本システムとパノラマ X 線画像ビューアおよび電子カルテ(レセコン)を連携することでカルテの自動入力を可能にするため，歯科医のカルテ作成に必要な労力を軽減し，診断全体 の効率化に貢献することができる．また歯科医の画像診断に AI の画像診断が加わることで画像診断における見落としを防ぐことも期待できる．ただし最終的なカルテの確定は歯科医が視診等を踏まえた診断のあとにカルテを修正して行うため，本システムは確定診断のために用いるものではなく，画像から明確に判断できうる情報を AI が事前に自動入力することを目的としている．本論文では本システムの有用性を検討するために，構築したデータベースとデータ作成のプロセスについて述べ，歯の状態診断に対する精度評価を行う．また AI システムと専門医の診断結果の比較を行う．


Emotional Similarity Word Embedding Model for Sentiment Analysis

We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pretrained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model.


Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors.


Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only.


Relationship Between Personality Patterns and Harmfulness: Analysis and Prediction Based on Sentence Embedding

This paper hypothesize that harmful utterances need to be judged in context of whole sentences, and extract features of harmful expressions using a general-purpose language model. Based on the extracted features, we propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representations of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model.

International Journal of Information Technology and Web Engineering


Construction and Expansion of Dictionary of Idiomatic Emotional Expressions and Idiomatic Emotional Expression Corpus

Objective: In the study of sentiment estimation from language, methods focusing on words, phrases, sentence patterns, and sentence-final expressions have been proposed. However, it is difficult to deal with a wide variety of emotional expressions by only assigning emotions to words and phrases. In particular, it is difficult to analyze metaphorical expressions and idiomatic expressions on a word-by-word basis, and it is impossible to register all expressions in a dictionary because new expressions can be created byflexibly replacing words. However, it is difficult to determine the constraints on the words to be replaced, and not all expressions can be registered in the dictionary as sentence patterns.Methods: In this paper, we construct a dictionary of idiomatic sentiment expressions, which contains idioms expressing emotions. In this paper, we construct a pseudo-emotional corpus by collecting utterances containing emotional idioms from social media and automatically assigning emotions expressed by the idioms.Results: This corpus includes expressions other than idioms, and can be an effective resource for estimating emotions in sentences that do not contain idioms. In this study, we create an emotion estimation model for utterances based on the constructed corpus, and conduct evaluation experiments to explore the problems of the idiomatic emotion corpus. In addition, using the constructed sentimentcorpus, we investigate how to expand the dictionary of sentiment expressions in idiomatic phrases by using deep learning methods.Conclusion: Using the corpus of idiomatic sentiments constructed by the proposed method as training data, models with and without idioms were constructed by machine learning models. The results show that the F-values of all emotions with idioms exceed 0.8. On the other hand, when idioms were not included, the F-values tended to decrease overall. However, the F-values of emotions such as "shame"and "excitement" were around 0.7, indicating that the characteristics of emotional expressions other than idioms were expressed.

International Journal of Computer & Software Engineering


Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of likes (favorites) and retweets (RTs) as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate


Toward Analyzing Relations between Sleep Time and Social Networking Service Texts: Prediction of the Tweet Time Span Using the Last Tweet of the Day

Sleeping habits are one of the major issues in today's healthcare. In this paper, we consider the problem of analyzing sleeping habits of people using social networking service (SNS) texts. As the first step toward predicting user's sleeping time using SNS texts, we assume that the time span between the user's last post in one day and the first post the next day can be used as a pseudo-indicator for the user's sleeping time if the user posts the text sufficiently frequently. We call such tweet time spans ``pseudo-sleeping time'' if the first tweet of the next day include ``Good morning'' or similar words. We try to predict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., the last tweet of the day). Preliminary experiments show that the tweet text contains some useful information to predict the user's pseudo-sleeping time.

International Journal of Advanced Intelligence (IJAI)


Estimating Psychological Impressions from Color Images Using Earth Mover's Distance

In this paper, we describe a method for estimating psychological impressions (e.g., Pretty, Elegant, Romantic, Cool, Formal, etc) from color images. Essentially, the Earth Movers Distance (EMD) is employed to map a given image onto the color image scale, a two-dimensional emotion space comprised of a WARM-COOL axis and a SOFT-HARD axis, on which many color combinations and Kansei words are located. To validate our proposed method, we collected real images from the Web and compared our EMD-based results with human evaluation results. The comparison demonstrated that EMD-based Kansei mapping is a promising method for extracting psychological impressions from color images.

本論文では，配色イメージスケールおよび言語イメージスケールを元に，画像イメージを簡便に評価できるシステムについて述べた．また，そのシステムによる評価結果と，人間の感性による評価結果の一致率を検討し，イメージスケール上のカテゴリーの再検討を行った．

Transactions of Japan Society of Kansei Engineering


Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT).

International Journal of Machine Learning and Computing


Factor Analysis of Individual Factors Associated with Low Arch Height Ratio of the Foot by SVM-RFE

SVM-RFEを用いた足部アーチ高比の低下を有する人の関連要因分析

Transactions of the Japanese Society for Medical and Biological Engineering


健常者の足背高測定による足部アーチ評価とアーチ高に影響を及ぼす因子

健常者の足背高測定による足部アーチ評価とアーチ高に影響を及ぼす因子


Twitter User's Hobby Estimation Based on Sequential Statements Using Deep Neural Networks

With more and more frequency, users communicate with each other on social media. Many users start on Twitter or Facebook to find friends who have the same hobby. Our study proposes a method to estimate the users interests (hobby) based on tweets on Twitter. One tweet does not, in and of itself, contain a lot of information, and some tweets are not related to the users hobby. Therefore, we propose a reliable hobby estimation method by extracting features from multiple, sequential tweets. The proposed method uses Recurrent Neural Networks (RNN) which can accommodate time-series information. We also used a Convolutional Neural Networks (CNN) which can treat contextual information. We used an averaged vector of word distributed representation as a feature. Using the proposed method based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), we obtained a 23.72% improvement as compared with a baseline method using a Random Forest (RF) regression as a machine learning algorithm.

近年，SNS上でのユーザー同士のコミュニケーションが増大している．TwitterやFaacebookを始める際，多くのユーザーが，同じ趣味の友達を探す．本研究では，Twitter上のツイートに基づいてユーザの興味(趣味)を推定する手法を提案する． 1つのツイートは，多くの情報を含んでいるわけではなく，また，ツイートの中にはユーザーの趣味に関係していないものもある．そこで，複数の連続したつぶやきから特徴を抽出し，信頼できる趣味推定法を提案する．提案手法では，時系列情報に対応できるリカレントニューラルネットワーク(RNN)を用いる．また，文脈情報を扱うことができる畳み込みニューラルネットワーク(CNN)も使用する．特徴量としては，単語分散表現の平均ベクトルを使用した．Long Short-Term Memory RNN (LSTM-RNN)に基づく提案された方法を使用して，機械学習アルゴリズムとしてランダムフォレスト(RF)回帰を使用するベースライン方法と比較して23.72%の改善を得た．


Emotion Recognition for Japanese Short Sentences Including Slangs Based on Bag of Concepts Feature Trained by Large Web Text

The growth of Internet communication sites such as weblogs and social networking sites brought younger people especially in teens and in their 20s to create new words and to use them very often. We prepared an emotion corpus by collecting weblog article texts including new words, analyzed the corpus statistically, and proposed a method to estimate emotions of the texts. Most slang words such as Youth Slang are too ambiguous in sense classification to be registered into the existing dictionaries such as thesaurus. To cope with these words, we created a large scale of Twitter corpus and calculated sense similarities between words. We proposed to convert unknown word to semantic class id so that we might be able to process the words that were not included in the learning data. For calculation similarities between words and converting the word into word cluster id, we used the word embedding algorithms such as word2vec, or GloVe. We defined this method as a method using Bag of Concepts as feature. As a result of the evaluation experiment using several classifiers, the proposed method was proved its robustness for unknown expressions.

Current Analysis on Instrumentation and Control


Slang Feature Extraction by Analyzing Topic Change on Social Media

Recently, we often see words such as youth slang, neologism and Internet slang on social networking sites (SNSs) that are not registered on dictionaries. Because the documents posted to SNSs include a lot of fresh information, they are thought to be useful for collecting information. It is important to analyze these words (hereinafter referred to as slang) and capture their features for the improvement of the accuracy of automatic information collection. This paper aims to analyze what features can be observed in slang by focusing on the topic. We construct topic models from document groups including target slang on Twitter by Latent Dirichlet Allocation (LDA). With the models, we chronologically analyze change of topics during a certain period of time to find out the difference in the features between slang and general words. Then, we propose a slang classification method based on the change of features.

CAAI Transactions on Intelligence Technology


ASCII Art Classification based on Deep Neural Networks Using Image Feature of Characters

In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expression using characters with visual technique. In this paper, we set up an experiment to classify AA pictures by using character features and image features. We try to clarify which feature is more effective for a method to classify AA pictures. We proposed four methods: 1) a method based on character frequency, 2) a method based on character importance value and 3) a method based on image features and 4) a method based on image features of characters. We trained neural networks by using these four features. In the experimental result, the best classification accuracy was obtained in the feed forward neural networks that used image features of characters.


Similarity Analysis and Repeating Pattern Detection in Fingerprint Features

Journal of Computational Methods in Sciences and Engineering


Intimacy Estimation of the Characters in Drama Scenario

演劇台本における登場人物間の親密度推定手法

Recently, a portable digital device equipped with voice guidance has been widely used, increasing the demand for the usabilityconscious dialogue system. One of the problems with the existing dialogue system is its immature application to non-task-orienteddialogue. Non-task-oriented dialogue requires some scheme that enables smooth and flexible conversations with a user. For example,it would be possible to provide topics related to a person who is familiar with a user or avoid providing topics related to a personwho is not a good friend with the user, by considering relationship with others in real life of the user. In this paper, we focus on thedialogue made by the two characters in a drama scenario, and tried to express their relationship with a scale of intimacy degree.There will be such various elements related to the intimacy degree as the frequency of response to the utterance and the attitude ofa speaker during the dialogue. We focus on the emotional state of the speaker during the utterance and try to realize an intimacyestimation with high accuracy. As the evaluation result, we achieved high accuracy in intimacy estimation than the existing methodbased on speech role.

近年，音声アシスタント機能を搭載した携帯型端末が普及し，より使い手に配慮した対話システムが求められている． 従来型の対話システムの問題点として，雑談のような非タスク型対話への対応が未熟な点があげられる．非タスク型対話 においては，ユーザとの会話を円滑かつ柔軟にするための工夫が必要となる．たとえば，ユーザの現実世界での人間関係 を考慮することによって，ユーザと親しい人物に関する話題の提供を積極的に行ったり，ユーザと親しくない(仲が良く ない)人物に関する話題の提供を避けたりすることができると考える．本論文では，演劇台本を題材に，対話中の2 者間 の人間関係を「親密度」という尺度により表現する．親密度に関わると考えられる要素として，発話の応答回数や発話中 の態度などがある．本論文では，その中でも発話中の感情状態に着目することで，高精度な親密度推定の実現を試みる． 評価実験の結果，発話役割に基づく従来手法を上回る高精度な親密度推定を実現することが出来た．

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics


Emotion Estimation Adapted to Gender of User Based on Deep Neural Networks

In this study, we focus on Twitter as a representative SNS and target emotion estima-tion from tweets posted on Twitter by male and female users. Specically, we constructgender-based emotion estimation models assuming that there are different word usage ten-dencies between genders. By analyzing gender-specic differences in the use of emotion-related slang and emoji, we propose a method to improve emotion estimation based onneural networks using a different distributed representation model for each gender. Ourevaluation experiments show that training with Deep Convolutional Neural Networks us-ing word's distributed representation as the feature produced higher estimation accuracythan training with Feed Forward Neural Networks.

International Journal of Advanced Intelligence (IJAI)


Index Generation of BGM Video Based on Distinctive Comments

The purpose of this study is to generate indexes for background music (BGM) based ondistinctive comments annotated to BGM videos posted on Nico Nico Videos. Our proposedmethod detects the end/start positions of the BGM considering the increase/decrease inthe number of comments, comments distinctive to the songs, and exclamatory expressionsannotated to the videos. The evaluation result indicated that the proposed method couldgenerate correct indexes within a 10 s error for BGM videos less than 40 min long andwith annotations of over 30,000 comments.


Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation

In recent years, flaming-that is, hostile or insulting interaction-on social media has been a problem. To avoid or minimize flaming, enabling the system to automatically check messages before posting to determine whether they include expressions that are likely to trigger flaming can be helpful. We target two types of harmful expressions: insulting expressions and expressions that are likely to cause a quarrel. We first constructed an original harmful expressions dictionary. To minimize the cost of collecting the expressions, we built our dictionary semi-automatically by using word distributed representations. The method used distributed representations of harmful expressions and general expressions as features, and constructed a classifier of harmful/general expressions based on these features. Anevaluation experiment found that the proposed method was able to extract harmful expressions with an accuracy of approximately 70%. The proposed method was also able to extract unknown expressions; however, it tended to wrongly extract non-harmful expressions. The method is able to determine unknown harmful expressions not included in the basic dictionary and can identify semantic relationships among harmful expressions. Although the method cannotpresently be applied directly to multi-word expressions, it should be possible to add such a capability by introducing time-series learning.

近年，ソーシャルメディア上での炎上が問題になっている．炎上を避けるためには，投稿前に自動的にメッセージを確認して，炎上を引き起こす可能性の高い表現が含まれているかどうかを判断することが役立つ．提案手法では，有害表現辞書を作成する．このさい，表現を集めるコストを最小限に抑えるために，単語分散表現を使用して半自動的に辞書を構築した．辞書に登録された有害表現と一般的な表現を用い，有害表現と一般的表現の分類を行った．評価実験により，提案手法は約70%の精度で有害表現を抽出できることが分かった．提案手法は未知の表現を抽出することもできたが，有害でない表現も有害として抽出する傾向があった．提案手法は，基本辞書に含まれていない未知の有害な表現を決定することができ，有害表現間の意味的関係を判定することができる．

International Journal of Technology and Engineering Studies


Review Score Estimation Based on Transfer Learning of Different Media Review Data

We propose a model to classify reviews based on review data from different media sources. Recently, research has been actively conducted on transfer learning between different domains with various kinds of big data as the target. The fact that evaluation expressions often vary in different domains presents a barrier to reputation analysis. Users commonly use various linguistic expressions to refer to creative works, depending on the specific media form.For example, the terms or expressions used in anime to describe creative works within that medium are different from the expressions used in comics, or games or movies. These differences can be considered as features of each individual medium. We should expect, then, that there would be differences in evaluation expressions among the various media, as well. We analyze the effects of such differences on classification accuracy by conducting transfer learning between review data from different media and demonstrate compatibility between the original (pre-transfer) and target (post-transfer) media by constructing a review classification model. As a result of our evaluation experiments, we are able to more accurately estimate review scores without using SO-Scores for training review fragments based on Long Short-Term Memory (LSTM) rather than using a method based on SO-Scores.

本研究では，異なるメディアのレビューデータに基づいたレビュー分類モデルの構築を提案します．現在，多種多様なビッグデータを対象とした異なるドメイン間での転移学習に関する研究が盛んである．異なるドメイン間では，評価表現が異なることが多く，評判分析の障壁となる．異なるメディアにおける著作物に触れているユーザが異なる表現を使用することは多い．たとえば，著作物に関して，「アニメ」や「漫画」，「ゲーム」，「映画」など，異なるメディアにおいては異なる用語や表現が存在する．評価表現以外にもメディアの違いの特徴が表れると考えられる．我々はメディア間でのレビューデータの転移学習をおこなうことで，こうした違いが分類精度にどんな影響を及ぼすかについて分析した．本研究では，著作物の媒体ごとに，転移先と転移元のメディアの相性について，レビューの評価分類モデルを構築することで明らかにした．また，評価実験の結果，Long Short Term Memoryを用いて，レビュー片の学習においてSo-Scoreを用いずに，SO-Scoreに基づく手法よりも正確に評価スコアの推定をおこなえた．


Emotion Recognition of Emoticon Based on Character Embedding

This paper proposes a method for emotion estimation of emoticon based on distributed representations of character meanings of the emoticon. The existing studies on emoticon have tried to extract emoticons from texts, estimate emotions of emoticons by breaking them up into constituent parts and by using their combination as feature. Based on the recently developed technique for word embedding, we try to realize versatile emotion estimation from emoticons by training the meanings of the characters constituting the emoticons and by using them as feature unit of emoticon based on the recent developed word embedding technique. The cross validation test was conducted for the emoticons emotion estimation model that was constructed based on the deep convolutional neural networks using distributed representations of characters as feature, and its result showed that our proposed method can estimate emotion of unknown emoticons with higher F1-score than the method based on the character n-gram appearance frequency vector as feature.

本研究では，顔文字の文字意味分散表現を用いた顔文字の感情認識をおこなう．従来の顔文字研究では，文中から顔文字を抽出したり，顔文字を構成パーツに分解しその組み合わせによる感情認識などがおこなわれてきた．近年発展してきた単語埋め込み技術を応用し，顔文字の文字単位での意味を学習し，特徴量として用いることで，より汎用的な顔文字の感情認識を試みる．文字の分散表現を素性として深層畳み込みニューラルネットワークに学習させる手法により構築した顔文字感情認識モデルを交差検証法により評価した結果，未知顔文字に対する感情認識を，単純な文字n-gram出現頻度ベクトルを素性として用いた手法よりも高い精度でおこなえることが分かった．


Constructing A Short Text Conversation system Based on the Relations Between Posts and Comments


An Improvement of Pedestrian Detection Method with Multiple Resolutions

In object detection, detecting an object with 100 pixels is substantially different from detecting an object with 10 pixels. Many object detection algorithms assume that the pedestrian scale is fixed during detection, such as the DPM detector. However, detectors often give rise to different detection effects under the circumstance of different scales. If a detector is used to perform pedestrian detection in different scales, the accuracy of pedestrian detection could be improved. A multi-resolution DPM pedestrian detection algorithm is proposedin this paper. During the stage of model training, a resolution factor is added to a set of hidden variables of a latent SVM model. Then, in the stage of detection, a standard DPM model is used for the high resolution objects and a rigid template is adopted in case of the low resolution objects. In our experiments, we find that in case of low resolution objects the detection accuracy of a standard DPM model is lower than that of a rigid template. In Caltech, the omission ratio of a multi-resolution DPM detector is 52% with 1 false positive per image (1FPPI); and the omission ratio rises to 59% (1FPPI) as far as a standard DPM detector is concerned. In the large-scale sample set of Caltech, the omission ratios given by the multi-resolution and the standard DPM detectors are 18% (1FPPI) and 26% (1FPPI), respectively.


Ego-state Estimation from Short Texts Based on Sentence Distributed Representation

Human personality multilaterally consists of complex elements. Egogram is a method to classify personalities into the patterns according to the combinations of the five levels of ego-states. With recent development of Social Networking Service (SNS), more researches have been trying to judge personality from the statements on SNS. However, there are several problems in the personality judgment based on the superficial information of the statements. Concretely, personality is not always reflected on every statement and the tendency of the statements with influence of personality changes with the times. It is also important to collect sufficient amount of statement data including the results of personality judgment. In this paper, to realize automatic egogram judgement, we focused on the short texts on the SNS, especially microblogs, and represented the comments on Twitter by distributed representation (sentence vector) in the pre-training. Then, we tried to create a model to estimate ego-state level of each user by using a deep neural network. The experimental result showed that our proposed method estimated ego-state with higher accuracy than the baseline method based on bag of words. To investigate the change of personality according to time, we also analyzed how the match rates of the estimation results changed before/after the egogram judgment. Moreover, we confirmed that the personality pattern classification was improved by adding a feature expressing the formal degree of the sentence.

人間の性格は，複雑な要素が組み合わさって，多面的に構成される．性格診断手法としてのエゴグラムは，5つの自我状態の高低の組み合わせに基づき，性格をパターン分類するものである．近年のSNSの発達にともなって，性格をSNS上の発言から診断しようという試みが増えてきた．しかし，発言の表層情報を手掛かりとした性格診断は，自我状態を推定するうえでいくつかの問題を抱える．具体的には，性格がすべての発言に反映されているわけではないこと，また，個人の性格に起因する発言の傾向は時間とともに変化していくものであるということなどがあげられる．また，十分な量の性格診断結果を伴う発言の事例を収集することも重要となる．本研究では，SNS，とくにMicroblog 上の発言(ショートテキスト)をもとに，エゴグラム診断を自動化するために，Twitter 上の発言を事前学習において分散表現(文ベクトル)により表し，Deep Neural Network による機械学習を用いて，ユーザごとに自我状態のレベルを推定するモデルの構築を試みる．評価実験の結果，提案手法による自我状態推定モデルは，Bag of Words を素性としたベースライン手法よりも高精度%であることを示した．また，時間経過による性格変化を調べるため，エゴグラム診断の前後において，推定結果の一致率にどのような変化が起きているかを分析した．さらに本論文では，文のフォーマル度合いを特徴づける素性を追加することで性格パターン分類の精度向上を確認した．


Conversion of Japanese Slang into Standard Japanese Considering Sensibility

感性を考慮した日本語俗語の標準語変換

With the recent spread of communication using social media, exchanging opinions each other on web has become more common irrespective of age and sex. On the other hand, a problem called as ``Internet flaming'' often occurs along with the increase of social network service users. One of the reasons might be that the users do not recognize meanings/intentions/emotions expressed by other users' words. In this study, we focused on slangs (Internet slangs) that are often used on SNS but are not registered in dictionaries, then tried to convert them into standard words. We also intended to output more appropriate candidates by considering not only semantic similarity but also affective similarity. The proposed method conducts filtering and re-ranking over the semantically similar candidates obtained based on distributed representations to detect the inappropriate candidates as standard word by focusing on two points: (1) features of slang/standard word and (2) affective similarity between the inputted word and the candidate words. In the evaluation experiment, the proposed method obtained a higher MRR than the baseline method.

近年のソーシャルメディアを用いたコミュニケーションの普及に伴い，Web上での意見交換が老若男女問わず一般的になってきた．一方で，「ネット炎上」と呼ばれる問題が，ソーシャルネットワークサービスのユーザの増加にともない，しばしば起こっている．この理由は，ユーザが単語により表現された意味/意図/感情を理解しないためである．本研究では，SNS上でよく用いられるが，一般的な辞書に登録されていない俗語(ネットスラング)に着目し，それらを標準語に変換することを試みる．我々は，意味的な類似だけでなく，感性的な類似を考慮し，より適切な候補を出力しようと考える．提案手法は，分散表現に基づき得られた候補を，2つの点に焦点をあて，フィルタリングし再ランキングする．入力語と出力候補間の，(1)俗語/標準語の特徴，(2)感性的類似度 の2点である．評価実験において，提案手法により，ベースライン手法よりも高いMRRを得た．

Transactions of the Japanese Society for Artificial Intelligence


Reidentification of Persons Using Clothing Features in Real-Life Video

Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification.

Applied Computational Intelligence and Soft Computing


Slang Analysis Based on Variant Information Extraction Focusing on the Time Series Topics

Recently, with increase of the number of users of Social Networking Sites, online communications have been more and more actively made, raising the possibility to use the big data on SNS for analyzing the diversity of language. Japanese language uses varieties of character types and such character types are combined and used for creating words and phrases. Therefore, it is difficult to morphologically analyze such words and phrases even though morphological analysis is a basic processing in natural language processing. Such words and phrases that are not registered in morphological analysis dictionaries are usually not defined strictly and semantic interpret for them seems to vary depending on deindividual. In this study, we chronologically analyze the topics related to slang on Twitter. In this paper, as a validation experiment, we conducted a topic analysis experiment chronogically by using the sequential tweet data, and discussed the difference of topic change according to the slang types.

近年，SNS の利用者増加に伴い，Web上でのコミュニケーションがよりいっそう活発になった． これにより，言葉の多様性について，SNS上のビッグデータを用いて解析できる可能性が出てきた．日本語では扱われる文字種が多いことからも，様々な表現が存在し，自然言語処理において基本的な処理である形態素解析が，比較的難しいことが問題となっている．こうした形態素解析の辞書に登録されていないような未知表現に対しては，厳密な定義が存在していないこともしばしばであり，意味解釈における個人差が大きいことが考えられる．本研究では，Twitter上の俗語に関連したトピックを時系列で分析する．本論文では，検証実験として，連続するTweetデータを用いたトピックの時系列分析実験をおこない，俗語の種類によるトピック変化の違いを考察する．変化しない情報(不変情報)が，一定期間においてどのような変化を示すかを実験により示し，標準語との差異を調べる．また，俗語の変化の仕方を変動値とし，特徴ベクトルとすることで，似たような傾向を示す俗語をクラスタリングする手法を提案し，実験結果を分析する．

International Journal of Advanced Intelligence (IJAI)


An Illustration Image Classification Focusing on Infrequent Colors

Illustration images used in comics or animation works have the style which is emotional feature. The style is represented by the various elements of the illustration image. There are a few researches which treat the style of illustration image is fewer. In this paper, we tried to model the style by using an image feature, and to classify an illustration image by the style. We thought that the color which appeared infrequently represent the style of illustration image. Therefore, we proposed the method for creating the color histogram which emphasize colors appearing infrequently. We called that as Infrequency histogram; IF-hist. To test effectiveness of the histogram, we experimented to classify the two styles which are For boys and For girls based on the IF-hist. As the result of the experiment, the case using the IF-hist, the precision of classification result by the style For girls is 93%. It is 50% higher than the case using existing normal color histogram. Moreover the precision of the other classification results have improved.

漫画やアニメ作品において用いられるイラスト画像は感性的な特徴である作風を持つ．作風はイラスト画像の様々な要素により表現される．イラスト画像の作風を扱った研究は少ない．本論文では，画像特徴量を用いることで作風のモデル化およびイラスト画像の作風による分類を試みた．我々は，イラスト画像においてあまり使用されない色が作風を表現していると考えた．そのため，あまり出現しない色を強調した色ヒストグラムを作成するための手法を提案する．我々はこのヒストグラムを「Infrequency histogram(IF-hist)」と呼ぶ．このヒストグラムの有効性を確認するため，我々は，IF-histを用いて「少年向け」と「少女向け」の2つの作風に分類する実験をおこなった．実験の結果，通常の色ヒストグラムを用いた場合よりも50%の精度改善を確認でき，本手法の有効性を示すことができた．


Pedestrian Re-identification using Color Feature in Multi Surveillance Video

In this paper we present a system to solve the problem of moving pedestrian re-identification in surveillancevideo. Surveillance video has low-resolution, high video noise and limited monitoring scope. Our proposed framework mustdeal with several problems such as variations of illumination conditions, poses and occlusions. How to extract the robustfeature that can adapt the problems have been the task. The people of global color approaches do not change in the processof monitoring. Our paper use the color histogram as feature descriptors and choose RGB HSV and UVW for color space.Traditional histogram method extract the global color approach as the feature. The object color structure information will beneglected. We use the SPM model supplement the structure information for the histogram. The results of a test from a realsurveillance system show that our method can provide a probability of matching.

International Journal of Advances in Electronics and Computer Science


A Study on the Review Analysis for the Automatic Evaluation of the Product

Nowadays, e-business has been never more popular and more convenient. Online merchants have flourished andmerchandize have diversified. However, its still difficult for users to quickly locate their desired products on online markets.Often times, an online shopper will evaluate ratings and comments from other users to speculate the current product. Inparticular, the easiest and the most intuitive way is to see the scores to determine the quality of the product. Therefore, if wecan automatically identify and analyze all the reviews and accurately and objectively give a score to a particular product, itwill be more efficient for users to search merchandize and for online business owners to manage the products they sell.

International Journal of Management and Applied Science (IJMAS)


種々のテキスト検索モデルの頑健性向上による音声ドキュメント検索の高精度化

種々のテキスト検索モデルの頑健性向上による音声ドキュメント検索の高精度化

We apply modifications to typical text retrieval methods based on vector space model, query likelihood model, and relevance model, to make them robust to out-of-vocabulary words and misrecognition. We propose novel query expansion methods and combination methods of syllable recognition-based retrieval with word recognition-based retrieval, for these typical methods. We used NTCIR-9 SpokenDoc task to evaluate them. Each modified method achieved better result than baseline. The methods based on stochastic models like the query likelihood model and the relevance model achieved better performance than the vector space model. The performance of our proposed methods was better than the best result published in NTCIR-9 competition.

テキスト検索に用いられてきた従来の3つの主な検索手法(ベクトル空間モデル,クエリ尤度モデル,適合モデルに基づく手法)に対し統一的な枠組みで改良を加えることで,音声ドキュメント検索における語彙外単語や音声認識誤りに対処する手法を提案し,比較検討を行った.各検索手法に対し,新たな検索質問拡張手法,および音節の3連鎖を単語と同様に扱う検索を単語単位の検索とスコアレベルで組み合わせる手法を提案する.提案手法の有効性をNTCIR-9のSpokenDocタスクで評価した結果,各手法でBaseline手法よりも検索性能が向上した.特に,確率に基づくクエリ尤度モデルに基づく手法と適合モデルに基づく手法では検索性能が高かった.提案手法はNTCIR-9で公表されている公式の最高精度の結果を上回る結果を得た.

Transactions of Information Processing Society of Japan


Extraction Japanese Slang from Weblog Data Based on Script Type and Stroke Count

Young people commonly use slang in the texts for weblogs or Social Networking Sites. How to treat such slang words properly is one of the problems in the field of text mining. In this paper, we examined several methods to extract Japanese slang called Wakamono Kotoba, which is particularly used by young people, by focusing on its script type and stroke count. In the evaluation experiment, a high precision was obtained when we adopted script type for extraction.

若者はWeblogやSNSにおいてよく俗語を用いる．こうした語をどう扱うかが，テキストマイニングなどの分野において，一つの問題となっている．本論文では，若者にとくに用いられる若者言葉と呼ばれる日本語俗語を文字種と画数に着目することにより抽出する手法を提案した．評価実験の結果，文字種を適用した場合に高い抽出精4度が得られることが分かった．


音声ドキュメント検索における種々の検討および線形補間係数を自動決定する検索質問拡張

本論文では，2011年に開催されたNTCIR-9ワークショップのコアタスクSpokenDoc内のSDRサブタスクに参加した際に行った音声ドキュメント検索に対する種々の検討を報告する．ベクトル空間モデルによるテキスト検索において，索引語の単位，索引語重みづけ方法，ベクトル空間に射影する音声認識候補数，検索質問拡張方法などの比較を行い，各ベクトル空間で計算される類似度を統合して音声ドキュメント検索を行う手法を提案した．


Three Predictions are Better than One: Sentence Multi-Emotion Analysis from Different Perspectives

Emotion prediction has been a core task in affective computing, which aims at finding the thorough human mental states byanalyzing peoples activities. In this paper, we focus on predicting emotions in the public online blogs from different people, byextracting as many reasonable emotions for each blog sentence as possible. Concretely, we consider three different perspectivesfor analyzing the multiple emotions in a sentence: (i) predict sentence emotions by examining the emotion related topics in aglobal sense; (ii) predict the sentence emotions from the context-sensitive word emotions; and (iii) predict sentence emotions byconsidering the emotional significance in the local bag of words. We build different probabilistic models from each perspective, toseparately generate the sentence emotion probabilities. We then integrate these probabilistic models to jointly predict the emotionprobabilities. Because the component models are based on different emotional assumptions with distinct features, the integratedpredictions should predict emotions from more general perspectives and therefore yield better results. In the experiment, weemploy different evaluation criteria to compare the multi-emotion predictions from the single and integrated models. Comparedto the results in the baseline model, our bi-integrated models achieve 8.69% higher Micro F1 and 7.78% higher Macro F1 scores,on average. Moreover, our tri-integrated model acquires 10.00% higher Micro F1 and 9.19% higher Macro F1 scores than thebaseline results, which proves our assumption, and suggests interesting features in the different emotion perspectives. © 2014Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

感情推定は，アフェクティブコンピューティングにおける核となるタスクの一つである．これは，人間の行動分析による人の精神状態を調べるために必要である．多くのブログ文には，著者の感情が表されているため，我々は，異なる人々のオンラインブログにおける感情推定に着目する．提案手法は，感情に関連するトピックに基づく感情推定と，文脈情報を用いた感情推定，また，局所的なBag of wordsを用いた感情推定を組み合わせたものである．また，我々は，既存の複数の確率的感情推定モデルを結合し，一つの感情に絞り込む方法を提案した．ブログコーパスを対象とした評価実験により，Micro F1スコアとMacro F1スコア双方において，提案手法が従来手法を上回る結果を得ることができた．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Emotion Estimation Method Based on Normalization of Prosodic Features by Using Synthesis Speech

合成音声を用いた特徴量の正規化による感情識別法

本論文では，音声からの感情推定において特徴量を正規化して識別を行う方法を提案する．従来感情識別に用いられている平均パワーやピッチといった韻律的特徴量は，感情による変化だけではなく発話内容そのものによって大きく変化する．そのため，たとえ同じ感情で発話されたとしても発話内容が異なれば異なる韻律となり，感情推定の性能低下を招き得る．そこで本論文では，平静の感情で同じ発話内容を発話した音声からも特徴量を抽出し，それとの差分に注目することで感情による特徴量の変化のみを抽出する方法を提案する．使用している5種類の特徴量(平均パワー，ピッチ等)ごとに減算による正規化，除算による正規化，正規化なし，の三つの方法で正規化し，全ての組み合わせの中で最も性能が向上する組み合わせを探索した．その結果，最適な組み合わせによる識別性能は，正規化を行わない従来法と比較して5.98%向上した．正規化法に関する分析を行ったところ，平均パワーは正規化が必要(演算は減算でも除算でも大きな差はない)であり，一方ピッチとMFCCは正規化なし，残りの二つの特徴量についてはどちらでも性能は大きくは変化しないことがわかった．

The Transactions of the Institute of Electronics, Information and Communication Engineers D

電子情報通信学会論文誌(D)


Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

We propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method (MEM). In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features.

本論文では，脳波特徴を利用した感性的画像検索システムのための，画像の好き嫌い(好悪)を分類する感性的フィルタリング技術の提案を行った．類似画像検索により得られた検索結果画像集合に対し，感性フィルタリングを行う方法として，画像特徴から推定した感性特徴量に基づく，最大エントロピー法(Maximum Entropy Method :MEM)を用いた画像の好悪分類を行う．本論文では，画像を見ている時の脳波から計算して得た相互相関特徴量を感性的な特徴量と考える．しかし，被験者が未知の画像を見るたびに脳波の測定を行わなければならないという問題がある．本論文では，正準相関分析に基づく線形回帰法を用いて画像特徴量から相互相関特徴量を推定するという解決策を提案する．

Applied Computational Intelligence and Soft Computing


Index Compression for Audio Fingerprinting Systems Based on Compressed Suffix Array

In this paper, we propose a method for index compression using a compressed suffix array. Taking advantage of the fact that the repetitive characters occur frequently in higher bits of the sorted audio fingerprint data, the proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. Vertical Coding is also used to compress the array, wherein the positions of the sorted data are stored.

圧縮接尾辞配列というデータ構造を利用し，オーディオ指紋に基づく検索のインデックスを圧縮する手法を提案した．ソートされたオーディオ指紋データが上位バイトは同じ値になりやすいという性質を利用し，同位8ビットに対するランレングス符号により圧縮を行う．また，データ順を示す配列が部分的に単調増加となる性質を利用し，Vertical Codeに基づく配列圧縮を行う．

International Journal of Information and Education Technology (IJIET)


Fast Search Method for Audio Fingerprinting Systems Based on Query Multiplexing

検索質問多重化による高速なオーディオ指紋検索

In music information retrieval, a huge search space has to be explored because a query audio clip can start at any position of any music in the database, and also a query is often corrupted by highly significant noise and distortion. Audio fingerprints have attracted much attention recently for providing compact representation of the perceptually relevant parts of audio signals. In this paper, we propose an extremely fast method of exploring a huge hamming space for audio fingerprinting systems. The effectiveness of our method has been evaluated by experiments using databases of 8,740 real songs and 800 artificially corrupted and 268 real queries.

楽曲検索では，ノイズ等による楽曲の劣化や，検索質問曲の演奏開始位置が不明であることから，一般に膨大な検索空間を探索する必要がある．多くの楽曲検索システムでは，楽曲の音響的・知覚的な特性に基づくオーディオ指紋(audio fingerprint) を特徴量として用いているが，本稿では，オーディオ指紋に適した高速検索手法を提案する．また，8,740 曲の楽曲データベースを用いた評価実験を行い，提案手法の有効性を示した．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Skelton Pruning Based on the Total Bisector Angle of the End Branches

Skeletonization is an important technique for shape representation, but the skeleton's high sensitivity to boundary noise hampers its application to automatic shape matching. In this paper, we propose a linear-time skeleton pruning algorithm based on the total bisector angle of the end branches. The main idea of the proposed algorithm is to iteratively delete the end branches having the smallest total bisector angle. Since the total bisector angle considers both the local and the global skeleton information, the proposed algorithm removes all redundant branches generated by boundary noise and obtains the ideal skeleton. The experimental results show that the proposed algorithm is highly adaptable, which means that under the same threshold conditions, the pruned skeletons obtained by our proposed algorithm are in accord with human visual perception for most shapes in the MPEG-7 and Tari 56 datasets. Consequently, the pruned skeletons can be applied to automatic shape matching.

画像の骨格化は，画像の形状を表現する上で重要な技術であるが，境界ノイズの影響を受けやすいという欠点があり，形状マッチングをする上で大きな問題点となっている．本論文では，終端枝角の二等分線に基づく，画像骨格線の枝刈りアルゴリズムを提案するとともに，実験においてその有効性を示した．

IEEJ Transactions on Electronics, Information and Systems


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．


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


Cluster-Based Reputation Model in Peer-to-Peer Network

This paper presents a cluster-based reputation model (CBRM) in Peer-to-Peer network. The model is consisted by reputation mechanism and cluster. In the model, we take the reputation mechanism for realizing the security transaction; and the network topology structure of CBRM adopts the cluster, so efficiency of reputation management is noticeably raised.

本論文では，ピアツーピア・ネットワークにおけるクラスタに基づく評判モデル(CBRM)を提案する．このモデルは，評判メカニズムとクラスタから構成され，評判メカニズムは安全なトランザクションを実現するために用いられる．また，CBRMのトポロジーはクラスタ型であるため，評判管理の効率が向上する．

International Journal of Machine Learning and Computing


Text-Independent Speaker Identification Based on Reducing Intersession Variability of Speech Feature Using PCA Transformation

In text-independent speaker identification, the phonetic variability and the intersession variability strongly afect the performance of speaker identification. In this paper, PCA transformation is utilized to reduce intersession variability of speech data in the observed space. The proposed method reduced the identification error rate by 42.6% compared with the conventional MFCCs and reduced the identification error rate by 27.2% compared with the MFB-PCA.

テキスト独立な話者認識において，音声変動やセッション間変動は話者認識の精度に大きな影響を与える．本論文では，PCA変換を用いることにより，音声データのセッション間変動を削減することを提案する．提案手法を用いることにより，MFCCを用いる従来手法に比べ，誤認識率を42.6%削減でき，MFB-PCAに基づく手法に比べ，誤認識率を27.2%削減できた．


An E-mail Filtering Method Based on Multi-attribute Values of Users Profile

E-mail is one of the often used communication methods; however, a lot of e-mail documents including useless information are distributed to computers. To solve this problem, we propose a method to filter out unimportant e-mail documents by judging the content of each document. Our method constructs personal profiles. They consist of multi-attribute values such as 'Sender', 'Theme' and 'Type' of each document. By using the profile, this method can distinguish whether a new e-mail document is important or not for the users. It is found that the accuracy of this method is higher than that of the traditional methods.

電子メールはコミュニケーションの手段として頻繁に使われているが，役に立たない多くのメールが行きかっている．この問題を解決するために，電子メールの内容に基づき，無益な電子メールをフィルタリングする手法を提案した．提案手法は，'Sender'，'Theme'，'Type'などの多属性値から個人プロファイルを作成することにより，新規の電子メールがユーザにとって重要か否かを判断する．

International Journal of Computer Applications in Technology


Fast Retrieval Algorithm for Earth Mover's Distance Using EMD Lower Bounds and Skipping Algorithm

The earth mover's distance (EMD) is a measure of the distance between two distributions, and it has been widely used in multimedia information retrieval systems, in particular, in content-based image retrieval systems. When the EMD is applied to image problems based on color or texture, the EMD reflects the human perceptual similarities. However, its computations are too expensive to use in large-scale databases. In order to achieve efficient computation of the EMD during query processing, we have developed fastEMD, a library for high-speed feature-based similarity retrievals in large databases. This paper introduces techniques that are used in the implementation of the fastEMD and performs extensive experiments to demonstrate its efficiency.

Earth Mover's Distance(EMD)は2つの分布間の距離尺度であり，画像検索を始めとするさまざまなマルチメディア情報検索において用いられている．我々は，EMDに基づく大規模データ検索を高速かつ効率的に行うためのライブラリfastEMDを開発した．本論文では，fastEMDの実装に用いられている技術およびfastEMDの性能評価実験について述べた．


A Method for Detecting Subtitle Regions in Videos Using Video Text Candidate Images and Color Segmentation Images

In this paper, a method for detecting text regions in digital videos with telop, such as drama, movie and news programming, is proposed. The typical characteristics of telop are that it does not move, and that its edges are strong. This method takes advantage of these characteristics to produce video text candidate images.

本論文では，ドラマや映画やニュースのような字幕付きの映像からテキスト領域を自動的に検出する方法を提案した．字幕は位置が不変で，エッジが強いという特徴を持っており，これらの特徴を利用することにより，テキスト領域の候補を生成する．

International Journal of Advanced Intelligence (IJAI)


Fast Multidimensional Nearest Neighbor Search Algorithm Based on Ellipsoid Distance

The nearest neighbor search in high-dimensional spaces is an interesting and important problem that is relevant for a wide variety of applications, including multimedia information retrieval, data mining, and pattern recognition. For such applications, the curse of high dimensionality tends to be a major obstacle in the development of efficient search methods. This paper addresses the problem of designing a new and efficient algorithm for high-dimensional nearest neighbor search based on ellipsoid distance. The proposed algorithm uses Cholesky decomposition to perform data conversion beforehand so that calculation by ellipsoid distance function can be replaced with calculation by Euclidean distance, and it improves efficiency by omitting an unnecessary operation. Experimental results indicate that our scheme scales well even for a very large number of dimensions.

高次元空間における最近傍検索は，マルチメディア・コンテンツ検索，データ・マイニング，パターン認識等の分野における重要な研究課題である．高次元空間では，ある点の最近点と最遠点との間に距離的な差が生じなくなるという現象が起こるため，効率的な多次元検索手法を設計することが極度に困難になる．本論文では，楕円体距離に基づく多次元検索に対し，きわめて高速な最近傍検索アルゴリズムを提案した．提案したアルゴリズムでは，楕円体距離に基づく距離計算をユークリッド距離に基づく距離計算に置き換えるために，まずデータ変換を行い，この後，ユークリッド距離計算において不要な演算を削除することにより，効率化を図る．実験結果によると，提案アルゴリズムは高次元の場合にも効率が劣化しないという特徴を持っている．


A New Method of Medical Image Retrieval based on Color-Texture Correlogram and GTI Model

This paper presents a method for endoscopic image retrieval based on color texture correlogram and Generalized Tversky's Index (GTI) model. First we define a new image feature named color texture correlogram, which is the extension of color correlogram. The texture image extracted by texture spectrum algorithm is combined with color feature vector, and then we calculate the spatial correlation of colortexture feature vector. Similarity metric is also the key technology during domain of image retrieval, GTI model is used in medical image retrieval for similarity metric, and the technique of relevance feedback is used in the algorithm to enhance the efficiency of retrieval. Experimental results show that the method discussed in this paper is much more effective.

本論文では，色テクスチャ・コリログラムと一般化Tverskyインデックス(GTI)に基づく内視鏡画像の検索手法について述べた．まず色コリログラムを拡張することにより，新しい画像特徴量である色テクスチャ・コリログラムを定義した．また画像検索においては画像の類似尺度が重要であるが，本論文では，医用画像検索の類似尺度としてGTIモデルを用いた．同時に，検索効率を向上させるために，関連性フィードバックも用いた．実験により，本論文で提案した手法に有効性を示した．

International Journal of Information Technology & Decision Making


Design Issue and Operation Method of the University Information Portal Based on the Existing Web Based System

徳島大学におけるポータルシステムの構成とその運用について

現在，高等教育機関において，学内の情報化を推進するため大学ポータルの導入が進んでいる．本稿では，大学ポータルのモデルを提案する．大学ポータルとは，単一のWEBベースのシステムではなく，分散するデータベースやWEBシステムが統合したシステムであり，学内情報の基盤となるフレームワークのことである．徳島大学の大学ポータルを事例として，大学ポータルの構築，運用に関する問題を具体的に考察する．運用の様子を示し，考察をおこなう．最後に，大学ポータルの課題について考察する．

Academic Information Processing Environment Research

大学情報システム環境研究


A Method to Retrieve Telops based on the Distance of Character Image Features between Queries and Telops

Video scene retrieval methods based on telop characters can retrieve the corresponding scenes to the query from the huge video data. In this paper, a new video scene retrieval method based on telop characters is proposed. In order to specify the suitable scene, this method recognizes the only corresponding telop characters to the query keyword, not all the characters. This method calculates the distance between each image features of telop characters and template image features of query keyword. The number of distance calculations can decrease by indexing the multi-dimensional data for image features of telop characters.

本論文では，映像内の文字情報である字幕に着目した映像シーン検索システムを提案する．本手法では，各字幕の文字画像特徴量と検索キーに対応する文字画像特徴量との距離に基づいて該当の字幕が出現するフレームを検索する．また，各字幕の文字画像特徴量を多次元索引化することで，検索キーの文字画像特徴量との距離計算を高速化する．さらに，本手法では検索過程で特徴量照合を行うため，前処理で文字認識処理が必要でなく，時間コストを軽減できる．


Construction of an Image Knowledge Database on WWW Image Retrieval Systems

This paper proposes a method to construction of an image knowledge database to store feedback information on WWW image retrieval systems. When the query has not yet been registered in the database, the image features of the true image selected by the user are registered in the database. On registration, the similarity of the features is considered, and only representative features are registered. On retrieval, this database is used as the supervisor data for the query, and images closer to the supervisor data are presented with priority.

WWW画像検索システムのフィードバック情報を格納するための画像知識データベースの構築方法について述べた．検索質問画像がデータベース中に存在しない場合，ユーザが選択した検索画像の特徴量をデータベースに格納する．また格納時には，特徴の類似性を考慮することにより，代表的な特徴のみをデータベースに格納する．検索時には，データベースを参照して，優先順位付きで結果を提示する．

International Journal of Computer Applications in Technology


An Improved Method to Select Candidates on Metric Index VP-tree

距離索引VP-treeにおける解絞込みの一改良手法

本論文では,距離索引の一つであるVP-treeの改良法を提案する.VP-treeは検索時にルートから検索範囲に適合するノードをたどり,最終的にたどり着いたリーフノードに格納されたデータとの距離を算出する.しかし,リーフノードにおける距離計算が増大すると検索速度が遅くなる.そこで,リーフノードにおける絞込み法に着目し,その改良法として検索入力に対する最近傍点を三角不等式の基準点として用いる手法を提案する.実際の画像データを用いて評価実験を行った結果,従来手法に比べ検索時間を5∼12%削減することができた.

The Transactions of the Institute of Electronics, Information and Communication Engineers D

電子情報通信学会論文誌(D)


Fast Multidimensional Nearest Neighbor Search Algorithm Using Priority Queue

Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of applications, including multimedia information retrieval, data mining, and pattern recognition. For such applications, the curse of high dimensionality tends to be a major obstacle in the development of efficient search methods. This paper addresses the problem of designing an efficient algorithm for high dimensional nearest neighbor search using a priority queue. The proposed algorithm is based on a simple linear search algorithm and eliminates unnecessary arithmetic operations from distance computations between multidimensional vectors. Moreover, we propose two techniques, a dimensional sorting method and a PCA-based method, to accelerate multidimensional search. Experimental results indicate that our scheme scales well even for a very large number of dimensions.

高次元空間における最近傍検索は，マルチメディア・コンテンツ検索，データ・マイニング，パターン認識等の分野における重要な研究課題である．高次元空間では，ある点の最近点と最遠点との間に距離的な差が生じなくなるという現象が起こるため，効率的な多次元検索手法を設計することが極度に困難になる．本論文では，線形探索アルゴリズムにおける距離計算中の不要な演算を削減することにより，きわめて高速な最近傍検索アルゴリズムを提案した．さらに，不必要な演算を早期検出するために，要素の分散値を用いた次元ソート法，並びに主成分分析に基づくデータ変換法を提案した．


検索質問と字幕の文字画像特徴量間の距離に基づく字幕検索手法

検索質問と字幕の文字画像特徴量間の距離に基づく字幕検索手法

In this paper, a new video telop retrieval method based on telop characters is proposed. This method calculates the distance between each image features of telop characters and template image features of query keyword. The number of distance calculations can decrease by indexing the multidimensional data for image features of telop characters. Experimental results, using 91 query keywords, show that the average precision of proposed method using 1-gram feature becomes 98.61%, and using 2-gram feature becomes 99.59%. Moreover, the retrieval time can be obtained in about 0.5 seconds when using 2-gram feature.

本論文では,高精度かつ高速な字幕検索手法を提案する.本手法では,各字幕の文字画像特徴量と検索キーに対応する文字画像特徴量との距離に基づいて該当の字幕が出現するフレームを検索する.また,各字幕の文字画像特徴量を多次元索引化するととで,検索キーの文字画像特徴量との距離計算を高速化する.実際に3時間分の映像データに対して映像中の出現頻度が比較的多い91単語を用いて検索実験を行った結果,1-gram特徴量を用いた場合には最大98.61%,2-gram特徴量を用いた場合には最大99.59%の平均適合率を得ることができた.

Transactions of Information Processing Society of Japan


Acoustic Model Adaptation Using Speech Synthesis for Codec Speech Recognition

符号化音声認識のための合成音声を用いた不特定話者音響モデルの適応法

現在,IP電話端末から音声認識システムを利用する場合,音声データ通信時における音声圧縮・伸張(符号化)の影響より固定電話と比較して音声認識精度が低下する問題がある.これを回避するため,符号化された多量の音声データを用いて音響モデルを再学習,及び適応する手法が提案されている.しかし,再学習や適応に必要となる多量の音声データを収集することは多大な労力と時間が必要となる.そこで本論文では,音声データの収集を必要としない音響モデル適応手法を提案する.本提案手法は,認識に用いる音響モデルから適応に必要な音声データを自己生成する手法である.本論文ではこの手法を実現するため,音響モデルの各分布より音素波形を合成し適応に用いる「合成音素波形による音響モデルの分布適応法」と連続音声を合成し適応に用いる「連続合成音声を用いた音響モデル適応法」を提案する.符号化方式G723.1を用いた音声認識実験結果より,連続合成音声を用いた音響モデル適応法は適応前の音響モデルの認識精度を改善することが分かった.これらの結果より,提案手法は適応データの収集を必要とせず,符号化音声を高精度に認識する手法として有効であるといえる.

電子情報通信学会論文誌(D)


Incorporate feature space transformation to content-based image retrieval with relevance feedback

In recent years, the employment of feedback information to improve image retrieval precision has become a hot subject in research field. But in the traditional relevance feedback methods, both relevant and non-relevant assigned information were required for the retrieval system. For the sake of practicality and convenience, this paper assumes that users only need to label the images from the previous query as relevant, which generates a new vector as feedback information. Through the feature space transformation, it is an adjustment in the spatial resolution of the feature space. The spatial resolution around relevant samples is contracted. Meanwhile, the analysis of the user's intension together with relevant forecast of the interested objects in the database make it posiible for the less similar vectors to get closer to the query vector and thus increasing query precision. Compared with the traditional relevance feedback approach, our method is shown to obviously improve the retrieval feedback performance.

画像検索における関連性フィードバック検索では，従来，ユーザが検索意図に関連した画像と同時に，関連しない画像も指定する必要があった．本論文では，より簡便に関連性フィードバック検索を行うため，ユーザの指定した検索結果中の関連する画像のみからフィードバック情報を生成する．特徴空間において，検索画像と関連した画像が近くなるように，空間の解像度を変えていくことにより，次第にユーザの意図する方向に特徴空間を変換する．従来手法との比較により，提案手法の優位性を示した．

International Journal of Innovative Computing, Information and Control


Identification of specific sequence motifs in the upstream region of 242 human miRNA genes.

We have identified novel over-represented and conserved motifs in the upstream regions of human and mouse miRNA stem-loop sequences by means of a new bioinformatic processing regimen. We observed sequence conservation -500 bp upstream in 189 human and mouse miRNAs declining with increasing distance from their putative miRNA stem-loop origin. We also found relatively GC-rich regions having more than 50% of guanine+cytosine (G+C) content at about -30 and -170 bp relative to human miRNA stem-loop sequence origin. To further identify specific sequence motifs that might be involved in the transcriptional regulation of miRNA precursors, we first searched 500 bp upstream sequences of 194 non-redundant human miRNA stem-loop sequences for frequently occurring motifs 5-15 bp long. We then found the comparable occurrences of the 20 most frequent motifs in the 2000 bp upstream regions of 242 human and 290 mouse miRNAs. The significantly reduced frequency of occurrence of all 20 motifs in the regions 2000 bp upstream of 23,570 human RefSeq genes demonstrated that these motifs were specific to the upstream miRNA sequences. The most frequently observed motif M1 (GTGCTTMTAGTGCAG), with a MEME E-value of 3.8e-57 was distributed within 500 bp upstream of stem-loop sequences and was also miRNA-specific. We suggest that these over-represented motif sites are good candidates for experimentally testing miRNA expression as well as possible interaction with regulatory factors.

本論文では，miRNAステムループ配列におけるmiRNA特異的上流配列モチーフの同定手法を提案する．提案手法は，ヒトmiRNA上流塩基配列からモチーフ検索プログラムにより検出された配列モチーフの出現頻度を，ヒトmiRNA上流塩基配列と比較対照となるマウスmiRNA上流塩基配列，ヒトタンパク質コーディング遺伝子流塩基配列間で比較・検定することにより，そのmiRNA特異性を検討し，既知転写因子結合部位との配列マトリックス比較により，その新規性を検討する．また，提案手法の有効性検証のために，遺伝子データベースを用いた,配列モチーフ同定実験を行った．


Real-timeFrequency Characteristic Normalization for Distributed Speech Recognition

分散音声認識における実時間周波数特性正規化手法

本論文では，分散音声認識における入力系の周波数特性の際による認識率劣化を抑制する周波数特性正規化手法として，複数参照ケプストラムを用いた実時間周波数特性正規化手法を提案した．


Similar Music Retrieval for the Query-by-humming Using the Earth Mover's Distance

Earth Mover's Distanceを用いたハミングによる類似音楽検索手法

In this paper, we propose a new similar music retrieval method based on humming query using the Earth Mover's Distance as the distance measure. Computing the EMD is based on a solution to the transportation problem, and the EMD is applied as the distance measure on similar image retrieval systems. In addition, we focus that the time complexity of the EMD is exponential worst case toward the number of notes, the improved method to decrease the number of notes in the music feature is also proposed.

本論文では,距離尺度としてEarth Mover's Distance (EMD)を用いたハミング検索手法を提案する.EMDは輸送問題における輸送コストの最適解であり,本手法では輸送問題における各供給地が有する資源量を各音符の音長,輸送コストを各音符の出現時間と音高情報から算出することで,リズムと音程との類似度を同じ距離尺度で計り,全体の曲調が類似した曲を検索する.さらに,EMDの計算量が音符数に対して指数関数的に増加することに着目し,検索精度を維持しつつ計算コストを低減可能な音楽特徴量を提案する.


WWW画像検索システムにおける有害画像フィルタリング手法

WWW画像検索システムにおける有害画像フィルタリング手法

In this paper, we propose a new filtering method using partial URL-based weighing scheme. The method first gathers a lot of hazardous URLs from search results of conventional WWW image retrieval systems by hazardous keywords. The method next decomposes a full URL into several partial paths, then estimates hazardous score for each partial path based on frequencies of paths. And it filters out hazardous images by distinguishing partial URL that there is possibility of hazardous. Experimental results show that the proposed method can improve the accuracy of filtering out hazardous images from search results of conventional systems.

本論文では,ユーザ・サイドで構築したURLデータベースを用いて,既存のWWW画像検索システムの検索結果から有害画像をフィルタリングする手法を提案する.本手法は有害画像を象徴する単語群を用意するだけでURLデータベースを自動構築する.URLデータベースにはURLのパスごとに有害度の重みづけを行い,有害性の高いURLを部分的に識別することで高精度なフィルタリングを実現する. Google Image Searchを対象として有害な画像が検索される27個の検索キーに対する評価実験を行った結果,F尺度として約70%のフィルタリング精度が得られた.


順位キューを用いた多次元データの高速近傍検索アルゴリズム

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Specific Speaker's Japanese Speech Corpus over Long and Short Time Periods

We have been collecting the Japanese speech corpus for investigating the relationship between intra-speaker speech variability and speech recognition performance. In this paper, we introduce our speech corpus. Our corpus consists of six speakers speech data. Each speaker read specific utterance sets three times a day, once a week. Using a specific female speakers speech data in this corpus, we conduct speech recognition experiments for investigating the relationship between intra-speaker speech variability and speech recognition performance. Experimental results show that the variability ofrecognition performance over different days is larger than variability of recognition performance within a day.

話者内音声変動と音声認識性能との関連性を調べるために，日本語の音声コーパスを長期間にわたり作成しており，本コーパスの概要について述べた．本コーパスは，6名の話者が週に1日，1日に3回，発話した内容を収録したものである．話者内音声変動と音声認識性能との関連性を調べるための実験を，1名の女性話者データを用いて行った結果，1日内の変動よりも複数日にわたる変動のほうが大きいことを示した．

Advances in Natural Language Processing Research in Computing Science


Simple PCAを用いたベクトル空間情報検索モデルの次元削減

Simple PCAを用いたベクトル空間情報検索モデルの次元削減

In this paper, we propose to use the Simple Principal Component Analysis (SPCA) for dimensionality reduction of the vector space information retrieval model. The SPCA algorithm is a data-oriented fast method which does not require the computation of the variance-covariance matrix. In SPCA, principal components are estimated iteratively so we also propose a criteria to determine the convergence. The optimum number of iterations for each principal component can be determined using the criteria. Experimentally, we show that the SPCA-based method offers improvement over the conventional SVD-based method despite its small amount of computation. This advantage of SPCA can be attributed to its iterative procedure which is similar to clustering methods such as $k$-means clustering. On the other hand, the proposed method which orthogonalizes the basis vectors also achieved much higher accuracy than the conventional random projection method based on $k$-means clustering.

本稿では，情報検索の代表モデルであるベクトル空間モデル (VSM; Vector Space Model)の次元削減手法として，Simple Principal Component Analysis (SPCA)を用いることを提案した． SPCAは簡単な繰り返し演算により，主成分を寄与率の大きい順に求めていく近似的な主成分分析手法であり，SVD等の行列演算を基本とする手法に比べ計算量が格段に少ないという特長がある． MEDLINEを用いた検索実験を行った結果，削減後の次元数によらず，SVDに比べて同等以上の平均適合率を得た． また，この実験における最高の平均適合率はSPCAで0.694(次元数40)， SVDで0.663(次元数50)であり，SPCAを用いることで誤り率は約1割 削減されている．

電気学会論文誌C (電子，情報，システム部門誌)


Image Database Retrieval Based on Relevance Feedback by Feature Space Warping

In recent years, the employment of the user's feedback information to improve the image retrieval precision has become a hot subject in research field. But in the traditional relevance feedback methods, both relevant and irrelevant user's assigned information was required for the retrieval system. For the sake of practicality and convenience, this paper advances that users only need to choose their inquired image files, which generate new index vector as relevance information. Through the feature vector space warping, the index is moved towards the user's inquiry intension. Compared with the traditional relevance feedback technique, the proposed method is shown to obviously improve the retrieval precision.

画像検索における関連性フィードバック検索では，従来，ユーザが検索意図に関連した画像と同時に，関連しない画像も指定する必要があった．本論文では，より簡便に関連性フィードバック検索を行うために，検索結果から，ユーザの必要な画像だけを指定していくことにより，次第にユーザの意図する方向に特徴ベクトルを漸近的に近づけていく手法を提案した．従来手法との比較により，提案手法の優位性を示した．


言語表現のベクトル空間モデルにおける最適な計量距離

言語表現のベクトル空間モデルにおける最適な計量距離

自然言語処理の手法にはコサイン距離, すなわち単純なユークリッド距離に依存しているものが多いが, この距離は素性間の相関を考慮しないこと, また素性に恣意的な重みづけを必要とするという問題をもっている.本論文ではこれに代わり, 訓練データより導かれる最適な計量距離を提案し, 二つの問題を同時に解決する.この計量は訓練データのクラスタ構造のひずみを最小化する二次最適化問題の解として導くことができる.提案する計量距離の効果を, 同義文検索, 文書検索, 及び機械学習用一般ベクトルデータのクラスタリングタスクによって確認し, ユークリッド距離に対し常に精度の改善が見られた.特にクラスタ化の強い同義文検索タスクにおいて精度の改善が大きく, ベースラインと比較して11点平均精度で33%の向上があった.

The Transactions of the Institute of Electronics, Information and Communication Engineers D-II

電子情報通信学会論文誌(D-II)


Channel Compensation Method for ETSI Standard DSR Front-end

ETSI標準分散音声認識フロントエンドにおける入力系の周波数特性正規化手法

本論文では，分散型音声認識方式における入力系の周波数特性の異なりによる音声認識精度の変動を抑制する手法として，平均一致化手法を提案した．提案手法は入力特徴量の平均と圧縮に用いるVQセントロイドとの平均を一致させ，入力系の周波数特性を正規化する．提案手法の有効性を検証するためETSIが勧告している分散型音声認識フロントエンドを用いた音声認識実験を行った結果，提案手法より周波数特性の変動による音声認識精度の低下を抑制することが可能であった．

電気学会論文誌C (電子，情報，システム部門誌)


Information Retrieval Using Non-negative Matrix Factorization

ベクトル空間情報検索モデルを用いた全文検索を行う場合，各文書を表現する索引語ベクトルがスパースになることが問題となる．そこで，本論文ではベクトル空間モデルの次元削減により情報検索精度を向上させる．次元削減には，非負行列である文書行列に対する次元削減手法として，Non-negative Matrix Factorization (NMF)を適用した．NMFは非負行列を2つの非負行列へ分解を行う手法である．提案手法は，NMFを用い非負行列である索引語文書行列を分解し，その一方の行列へ索引語文書行列を射影することにより，次元削減を行う手法である．MEDLINEを用いた情報検索実験で提案手法の有効性を示した．

電気学会論文誌C (電子，情報，システム部門誌)


Development of a WWW Image Retrieval System Using the Image Knowledge Database

画像知識データベースを用いたWWW画像検索システムの開発

ユーザからのフィードバック情報を格納した画像知識データベースを用いたWWW画像検索システムを開発した．本システムでは，既存システムの結果からユーザが選択した正解画像の画像的特徴量をフィードバック情報としてデータベースに登録することで，次回の検索の際，このデータベースを教師データとして，より類似している画像を優先的に提示することができる．従来システムとしてGoogle Image Searchを用い，130個の検索キーに対する評価実験を行った結果，11.6%の精度向上が確認できた．

The Transactions of the Institute of Electronics, Information and Communication Engineers D-I

電子情報通信学会論文誌(D-I)


Efficient Multidimensional Indexing Using One-dimensional Self-Organizing Maps

1次元自己組織化マップを用いた高次元データの高速近傍検索

高次元空間における最近傍検索は，マルチメディア·コンテンツ検索，データ·マイニング，パターン認識等の分野における重要な研究課題であるが，効率的な多次元インデキシング手法は，従来，存在しなかった．本論文では，自己組織化マップを用いて，高次元空間での近傍関係をできる限り保ちつつ，高次元データを1次元空間へ配置し，1次元マップから得られる情報を用いて探索範囲を限定することにより，きわめて高速な最近傍検索手法を提案した．


Improvement of vector space information retrieval by using long-term relevance feedback

This paper proposes a new method to improve information retrieval performance of the vector space model (VSM) by long term preservation of user-supplied relevance information in the system. The proposed method incorporates user's relevance feedback information and information of original documents similarities into the retrieval model that is build by using a sequence of linear transformations. The results of our experiments show that the proposed method has better retrieval performance and provides an approach that enables preservation of user-supplied information in the system.

従来のベクトル空間モデルを情報検索に応用すると検索精度の低下がある．本論文では，長期的にベクトル空間モデルに基づく検索システムにユーザからのフィードバック情報を残す新しい方法を提案する．検索精度を高めるため，ユーザからの適合性の判定結果だけではなく，文書間距離などの情報も検索モデルに組み入れ，線形変換によりベクトル空間検索モデルを構築する．本論文で提案した手法の有効性を確かめることができた．


Relevance Feedback Using Support Vector Machine for Information Retrieval

サポートベクターマシンによる適合性フィードバックを用いた情報検索

本論文では，ベクトル空間情報検索モデルを用いた全文検索精度の向上を目指した．本論文では，サポートベクターマシンを用いた適合性フィードバック手法を提案した．提案手法は，利用者からの適合·不適合の情報をもとにサポートベクターマシンを学習させ，利用者が要求する検索文書を識別し，不適合文書を利用者に提示しない手法である．日本語全文検索テストコレクションを用い，提案手法の有効性を検証する実験を行い，提案手法が全文検索に有効であることを示した．

Transactions of Information Processing Society of Japan


An information retrieval model based on vector space method by supervised learning

This paper proposes a method to improve retrieval performance of the vector space model in part by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, information such as original document similarities is incorporated into the retrieval model, which is built by using a sequence of linear transformations. High-dimensional and sparse vectors are then reduced by singular value decomposition and transformed into a low-dimensional vector space, namely the space representing the latent semantic meanings of words. The method has been tested with two test collections, the Medline collection and the Cranfield collection.

本論文は，部分問題の質問に適切なドキュメントのユーザに供給された情報の利用により，ベクトル空間モデルの検索パフォーマンスを改善する方法を提案する．ユーザの関連フィードバック情報に加えて，原本類似性のような情報は検索モデルに組み入れられる．それは一次変換のシーケンスの使用により構築される．その後，高次元で希薄なベクトルは特異値分解によって縮小され，低次元のベクトル空間(すなわち言葉の潜在している意味的な意味を表わすスペース)に変形される．提案した手法によりいろいろなテストを行ったが，その有効性を証明することができた．


長期的適合性フィードバックによるベクトル空間モデルの精度改善

長期的適合性フィードバックによるベクトル空間モデルの精度改善

本論文では，ユーザからのフィードバック情報を，長期的にベクトル空間モデルに基づく検索システムに残す方法を提案する．提案するモデルでは，検索精度を高めるため，ユーザからの適合性の判定結果だけではなく，文書間距離などの情報も検索モデルに組み入れ，線形変換によりベクトル空間検索モデルを構築する．その後，ベクトル空間モデルの高次元行列を特異値分解により縮退し，低次元のベクトル空間，つまり索引語と文書の潜在的な意味構造を表した意味空間に変換する．提案手法により，実験システムを構築し，二つのテストコレクションを用いて，評価実験を行った．本論文で提案した手法の有効性を確かめることができた．


教師あり学習によるベクトル空間情報検索モデルの精度改善

教師あり学習によるベクトル空間情報検索モデルの精度改善

本論文では，教師あり学習によるベクトル空間情報検索モデルの精度改善手法について述べた．提案手法を用いて，線形変換により検索質問集合に対する適合性情報および文書間距離情報を検索モデルに組み入れることにより，ユーザからのフィードバック情報を長期的に検索システムに反映させることが可能となった．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


PPM*言語モデルを用いた日本語単語分割

PPM*言語モデルを用いた日本語単語分割

日本語の単語分割の精度を向上させるために，データ圧縮アルゴリズムPPM* (Prediction by Partial Matching)を用いた言語モデルを適用した単語分割手法を提案した．PPM*は，確率·統計的テキスト圧縮技報として優れており，無限長文脈を取り扱うことができるという利点を持っているため，通常の文字n-gramモデルよりも頑健な単語分割を行うことができるようになった．


文字クラスモデルによる日本語単語分割

文字クラスモデルによる日本語単語分割

日本語処理において，単語の同定，すなわち文の単語分割は最も基本的かつ重要な処理である．本論文では，日本語文字のクラス分類により得られた文字クラスの確率モデルを用いた新しい単語分割手法を提案した．また，対話文データベースを用いた評価実験の結果，文字クラスモデルは単純な文字連鎖の確率モデルよりも，高い精度を達成できることを示した．


単語の位置情報に基づくコーパスからのコロケーションの自動抽出

単語の位置情報に基づくコーパスからのコロケーションの自動抽出

コーパス中の単語位置情報に基づき，連語表現(コロケーション)を自動抽出する手法を提案した．提案した手法では，コーパス中に各単語の位置情報を用いて，任意の文中のコロケーションを連続型・不連続型の別に抽出する．実験の結果，連続型・不連続型コロケーションともに重要な表現が抽出されており，提案した手法の有効性を示すことができた．


発話タイプ付きコーパスを用いた確率的対話モデルの自動生成

発話タイプ付きコーパスを用いた確率的対話モデルの自動生成

高次の言語情報である対話に対する確率的モデルを，エルゴード HMM および確率オートマトンを用いて，コーパスから自動的に生成するための方法について述べた．これらの確率的対話モデルを用いることにより，話者の交替や質問・応答・確認といった会話の基本的な構造を確率・統計的な観点からモデル化することができた．また，得られた確率的対話モデルを，情報理論的な観点から評価した．


確率的言語モデルに基づく多言語コーパスからの言語系統樹の再構築

確率的言語モデルに基づく多言語コーパスからの言語系統樹の再構築

各言語の言語データから確率的言語モデルを構築し， 次に確率的言語モデルの間に導入した距離に基づき， 元の言語に対するクラスタリングを実行する方法を提案した． 提案した方法を用いることにより，言語系統樹の構築を自動的に行うことが できることを示した．


Collocations in Language Learning: Corpus-based Automatic Compilation of Collocations and Bilingual Collocation Concordancer

In this paper, we are primarily concerned with the acquisition of collocational knowledge from corpora to use it in the language learning process. We present an efficient method from extracting collocations from corpora which uses the cost criteria measure and a tree-based data structure. We also propose a bilingual collocation concordancer, a tool that provides language learners with collocation correspondences between a native language and a foreign language.

大規模な言語データであるコーパスから連語表現(コロケーション)を自動抽出する方法，および 抽出された表現を外国語学習に適用する方法について述べた．連語表現をコーパスから効率的に抽出するために，仕事量基準と木構造を用いる方法を提案した．また，学習者に対し，母国語と外国語の間で，対応する連語表現を並べて提示できるようなツール(bilingual concordancer)を提案した．

Journal of Computer Assisted Language Learning


Mixture Probabilistic Context-Free Grammar: An Improvement of a Probabilistic Context-Free Grammar Using Cluster-Based Language Modeling

This paper proposes an improved probabilistic CFG, called mixture probabilistic CFG, based on an idea of cluster-based language modeling. This model assumes that the language model parameters have different probability distributions in different topics or domains. The basic idea of this model involves clustering a training corpus into a number of subcorpora, and then training probabilistic CFGs from these subcorpora. The resulting probabilistic CFGs become context- or topic-dependent, and thus accurate language modeling would be possible.

自然言語の確率·統計的モデルとして，混合確率文脈自由文法という新しい言語モデルを提案した．このモデルは，言語モデルのパラメータはトピックやドメインにより異なった確率分布を持っていると仮定している．混合確率文脈自由文法では，学習データを幾つかのクラスタに分割し，各クラスタから学習した確率文脈自由文法を組み合わせることにより，場面·状況依存の言語現象を取り扱うことができるという利点がある．


HMMと一般化LR構文解析法を用いた実時間大語彙連続音声認識装置の実現

HMMと一般化LR構文解析法を用いた実時間大語彙連続音声認識装置の実現

大語彙の連続音声認識を実時間で処理するための，HMM-LR 連続音声認識装置の設計，開発及び処理性能について述べた．処理量が極めて大きな継続時間制御付きトレリス計算を伴うHMM-LR連続音声認識を実時間で実行するために，本装置は種々の高速化手法を33個のDSPを用いて実現した．その結果，連続文節発声の入力音声に対し，入力文の長さに依らずに，発声終了後から2∼3秒ですべての認識処理を終了する処理速度を達成した．

The Journal of the Acoustical Society of Japan


仕事量基準を用いたコーパスからの定型表現の自動抽出

仕事量基準を用いたコーパスからの定型表現の自動抽出

自然言語において頻繁に使用される定型的な表現をコーパスから自動的に抽出する基準として仕事量という概念を提案した．仕事量は，いくつかの単語を一まとまりの単位と考えることにより，各単語を別個に処理するよりも，どれだけの処理が削減できるかということを定量的に測る尺度である．また，仕事量基準を用いた定型表現の抽出方法について述べ，提案した方法が有効であることを実験的に示した．

Transactions of Information Processing Society of Japan


Processing Unknown Words in Continuous Speech Recognition

This paper proposes a continuous speech recognition method which accepts any utterance that might include unknown words. In this method, words not in the lexicon are transcribed as phoneme sequences, while words in the lexicon are recognized correctly.

音声認識における未知語処理方法として，タスクを記述する通常の文法と，音韻間の制約を記述した文法の 2 つを 用いることにより，未知語の部分を音韻のつながりとして認識する方法を提案した．

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences


Continuous Speech Recognition Using Two-Level LR Parsing

This paper describes a continuous speech recognition system using two-level predictive LR parsing. In Japanese, a sentence is composed of several phrases, thus two kinds of grammars, namely an intra-phrase grammar and an inter-phrase grammar are sufficient for recognizing sentences. Two-level predictive LR parsing makes it possible to use not only intra-phrase grammatical constraints but also inter-phrase grammatical constraints during speech recognition. The system is applied to Japanese sentence recognition where sentence were uttered phrase by phrase, and attains a word accuracy of 95.9% and a sentence accuracy of 84.7%.

日本語の音声認識に適した方法として， 文節内および文節間の 2 つのレベルの文法を段階的に 用いる 2 段階 LR 解析法を提案した． また，日本語の音声認識評価実験を行い，単語認識率95.9%，文認識率84.7%という高い精度を達成した．


HMM Speech Recognition Using Stochastic Language Models

In this paper, the following three stochastic language models are investigated for an HMM continuous speech recognition system. (1) Trigram model of Japanese syllables, (2) Stochastic shift/reduce model in LR parsing, and (3) Trigram model of context-free rewriting rules.

言語的な尤度を計算するのに有効な確率的言語モデルとして， 日本語音節の連鎖統計情報，確率文脈自由文法，文脈自由文法の生成規則の連鎖統計情報 の 3 つのモデルを音声認識に適用し，各モデルの有効性を示した．

The Journal of the Acoustical Society of Japan


HMM-LR音声認識システムの性能評価

HMM-LR音声認識システムの性能評価

HMM 音韻認識と拡張 LR 構文解析法を統合化した HMM-LR 音声認識システムに セパレートベクトル量子化，HMM の継続時間長制御，VQ コードブック·マッピングに基づく話者適応 を組み入れることによりシステムの改良を行った． 日本語文節認識実験で評価を行い，システムの有効性を示した．


HMM音韻認識と拡張LR構文解析法を用いた連続音声認識

HMM音韻認識と拡張LR構文解析法を用いた連続音声認識

高精度の音声認識を実現するためには，言語的な情報の利用が不可欠である． 本論文では，文脈自由文法に基づく統語的(文法的)な制約を音声認識時に用いる方法について検討し， 拡張 LR 構文解析法と HMM (Hidden Markov Model)を用いた 音声認識手法を提案した．提案した手法では，音声認識と言語処理の間に音韻ラティス等の中間的なデータを介さないため，高精度のかつ効率的な認識処理系を構成することができるという特徴を持っている．


直観主義的解析学の諸原理

直観主義的解析学の諸原理

本論文は，直観主義論理的論理に基づく解析学である直観主義的解析学での公理の意味を明らかかにすると共に，直観主義的解析学の公理系から，古典的解析学では成立しえない 定理をいくつか導き，これらの定理の意味論的立場での解釈について論じた．


徳島大学キャンパスネットワークシステムの更新について

徳島大学キャンパスネットワークシステムの更新について

徳島大学で平成22年3月に行われたキャンパスネットワークの更新について述べた．ネットワークのさまざまな問題点を調査し，可用性向上，帯域拡大，高機能化，低運用コスト化等の課題について検討し，更新を行った．

Academic Information Processing Environment Research

大学情報システム環境研究


Distance Metric Learning for Content-based Image Retrieval

画像検索のための距離尺度の学習

The performance of a content-based image retrieval system is inherently constrained by the low-level features adopted to represent the images in the databases. Machine learning and statistical modeling approach is a simple yet effective technique for image retrieval, but the retrieval performance is often dependent on a large number of training data. The method presents an optimal metric distance function based on a simple cluster and a little training data by learning a distance metric that respects these relationships. Experiments show that the learned metrics can be used to significantly improve image retrieval performance.

画像検索システムの性能は，データベース中の画像を表現する低レベルの特徴量によって決められる．本論文では，単純なクラスタと少量の学習データから，特徴量間の距離尺度を自動的に学習する手法を提案する．自動学習により得られた距離尺度を用いることにより，画像検索性能を改善することができた．


Analyzing Transformers via Value Matrices

Transactions of the Japanese Society for Artificial Intelligence


URLの類似性に着目したWWW空間からの関連語自動収集手法

URLの類似性に着目したWWW空間からの関連語自動収集手法

本論文では，シーズとなる単語群(基底単語群)の関連語をWWW空間から効率的に自動収集する手法を提案する．基底単語群が存在するページのURL集合と関連語が存在するURL集合間に類似性があると予測される．そこで本手法では，基底単語群を既存のWWW検索システムに入力することで得られる検索結果のURLをデータベース化し，基底単語群のURLデータベースと類似性を計算することで，関連語を効率的に収集する．

日本データベース学会Letters

日本データベース学会Letters