Chinese Lexical Analysis Based on Hybrid MMSM Model

In this paper, we describe a scheme for Chinese word segmentation and POS tagging which integrates the character-based and word-based information in the directed graph generated by the MMSM-model. Word-level information is effective for analysis of known words, while character-level information is useful for analysis of unknown, words. A Hidden semi-CRF model is proposed for the unknown words detection. and POS tagging. The proposed Hidden semi-CRF has two state chains with unequal states which Can perform segmentation and POS tagging of unknown words simultaneously. The hybrid model was evaluated using the test data from SIGHAN-6 and achieved higher F-score than the stage-of-the-art models.


Facilitative Effect of the Picture Superiority Effect During Encoding and Retrieval

In order to investigate the picture superiority effect, we compared the ERP between picture combined word (picture-word) and pure word (word) during encoding and retrieval. During encoding, FN400 was more negative and lasted longer for picture-word than for word. Late positive component (LPC) was more positive and distributed broadly for word compared to picture-word. During retrieval, old picture-word elicited remarkably FN400 familiarity effect and parietal old/new effect compared to old word. We suggested that simultaneous image and verbal encoding of picture-word elicited better and faster recollection compared to word during the memory test. Our results demonstrated that the picture superiority effect was related to the ability of pictures enhancing encoding and facilitating recollection.


AN CLUSTERING APPROACH FOR COMPLEX EMOTION RELATED CATEGORIZATION

An affective computing, as a branch of artificial intelligence, attracts attention as a popular growing filed with many applications insofar as information retrieval, e-learning and human computer interaction. But until now the fine-grained theory of emotion is still a challenge. In this paper, a novel method to analyze emotion related category of contemporary Chinese.


Word Sense Indicators: Effective Feature for Chinese Word Sense Disambiguation

This paper presents a statistical method to extract word sense indicators as semantic disambiguators. Word sense indicators are collected based on selecting the best seeds and retrieving collocations. The similarities are comparied between sets of sense indicators for tagging sense. Our experiments showed that one or two indicators around the ambiguous word can help to recognize its sense, and sense indicators are mainly appear in ±3 context window size in Chinese.

Information : an International Interdisciplinary Journal


Function Labeling for Unparsed Chinese Text

This paper presents a work of function labeling for unparsed Chinese text. Unlike other attempts that utilize the full parse trees, we propose an effective way to recognize function labels directly based on lexical information, which is easily scalable for languages that lack sufficient parsing resources. Furthermore, we investigate a general method to iteratively simplify a sentence, thus transferring complicated sentence into structurally simple pieces. By means of a sequence learning model with hidden Markov support vector machine, we achieve the best F-measure of 87.40 on the text from Penn Chinese Treebank resources - a statistically significant improvement over the existing Chinese function labeling systems.

本論文では，未解析中国語テキストのための機能ラベル付け手法を提案する．フル解析木を利用する従来の手法とは異なり，本提案手法では直接，十分な解析リソースが不足した言語のため容易に拡張される語彙情報に基づいて，機能ラベルを認識するための効果的な方法を提案した．既存の中国語機能ラベリングシステムに比べて統計学的に有意な改善が得られた．

IEEJ Transactions on Electronics, Information and Systems


Characteristics of Event-related Potentials in Recognition Processes of Japanese Kanji and Sentences for Chinese Bilinguals

This paper investigates the recognition process of Japanese kanji and sentences for Chinese bilinguals 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 the Japanese sentences condition. The results demonstrated that the neural mechanisms of recognition processes of Japanese sentences including kana between the two groups were not identical. When recognizing ambiguous sentences, Chinese bilinguals' P600 only appeared over the right frontal lobe, reflecting that syntactic integration and revision of ambiguous sentences for Chinese bilinguals was related with the right hemisphere. The results showed, for Chinese bilinguals, the difficulty of Japanese language learning was the recognition and understanding of Japanese sentences with kana, not Japanese kanji. We would like to provide a scientific learning method for Chinese bilinguals to enhance their Japanese language learning efficiency from the aspect of brain science.


Automatic Text Summarization Using Support Vector Machine

This work investigates different text features to select the best one and proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position,sentence centrality, sentence resemblance to the title, sentence inclusion of name entity,sentence inclusion of numerical data, sentence relative length, Bushy path of the sentenceand aggregated similarity for each sentence to generate summaries. First we investigatethe effect of each sentence feature on the summarization task. Then we use all featuresscore function to train Support Vector Machine (SVM) in order to construct a text summarizer model. The proposed approach performance is measured at several compressionrates (CR) on a data corpus composed of 100 English articles from the domain of politics.

International Journal of Innovative Computing, Information and Control


Wikipedia Dased Semantic Related Chinese Words Exploring and Relatedness Computing

This paper introduces our way of finding semantic related Chinese word pairs from the open encyclopedia Wikipedia and analyzing the degree of semantic relations. Almost 50,000 structured documents are collected from Wikipedia pages. Then considering of hyperlinks and text overlaps etc., about 400,000 semantic related pairs are employed. We roughly measured the semantic relatedness using the position and frequency information in the documents. With comparing experiment on data sets with different degrees of semantic relations using some other classic algorithms, we analyze the reliability of our measures and other properties.

Journal of Beijing University of Posts and Telecommunications


Chinese Lexical Analysis Based on Hidden Semi-CRF

In order to solve problems of the Chinese word segmentation and POS tagtaggingwhich are still existing in Chinese lexical analysis, a Hidden semi-CRF model, whichhas two chains of states with unequal number of states, is proposed for the Chinese lexicalanalysis. The Hidden semi-CRF detects the words together with their part-of-speech reregardlesswhether the words are in the system dictionary or not. A new-words-generatingframework is also built for training and testing, under which the definition and distridistributionof the new words conforms to the characteristic of the ones in real text. Theproposed framework enhances the performance of new words detecting and POS tagging,so that the overall precision of the system for Chinese lexical analysis could be furtherincreased. The experiment results show that the proposed method is capable of detectingeven low frequency new words, which in return increases the overall precision of Chineseword segmentation and POS tagging in Chinese lexical analysis.

ICIC Express Letters, An International Journal of Research and Surveys


Effect of Using Pragmatics Information on Question Answering System of Analects of Confucius

In general, the techniques of statistical retrieval and shallow language analysis are chiefly used in question answering(QA) systems in order to improve the accuracyof answers. But these techniques are not contributing effectively to restricted domain QAsystem of classic Chinese literature such as Analects of Confucius. This QA systemrequires to extract related verses and automatically answer the query in natural Chineselanguage about Confucius thought in the Analects of Confucius. Therefore we propose a novel method to integrate the pragmatics information with classical informationretrieval technique for QA system so as to improve retrieval efficiency. In this paper,we examined the effect of the pragmatics information on QA system of the Analectsof Confucius. According to the experiments, the pragmatics information based retrievalresults are more accurate than the one without using it.

一般的に，質問応答システムでは統計的検索と浅い言語解析の技術は使われている．しかし，これらの技術は，孔子論語のような古典的な中国文学に対してあまり効果が無い．本論文では，検索効率を向上させるために質問応答システムにと語用論の情報を活かした新規な方法を提案する．実験により，提案した手法の有効性を確かめた．


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


Exploring Words with Semantic Relations from Chinese Wikipedia

This paper introduces a way of exploring words with semantic relations from Chinese Wikipedia documents. A corpus with structured documents is generated from Chinese Wikipedia pages. Then considering of the hyperlinks, text overlaps and word frequencies, word pairs with semantic relations are explored. Words can be self clustered into groups with tight semantic relations. We roughly measure the semantic relatedness with different document based algorithms and analyze the reliability of our measures in comparing experiment.

Information : an International Interdisciplinary Journal


Retrieval Method of Example Sentence for Intelligent English Composition Support System

Recently the opportunity of reading and writing in English has been greatly increasing. We focused on supporting the Japanese to write English sentence by indicating appropriate example sentences. This paper proposed a method to retrieve such target example sentences by using domain categories and keywords or directly inputting the Japanese sentence.The result of the experiment to evaluate the precision of example sentence retrieval indicated that our technique exceeded the conventional technique by more than 20%.

近年，英語での読み書きの機会が増加している．本研究では例文ベースの英作文支援技術を提案し，システムで用いる例文データベースの効果的な検索手法について，複数の従来手法と比較することにより評価をおこなった．


The Effects of a Classic Self-Learning System Utilizing Pragmatics Information and Topic

The purpose of this study is to construct a new self-learning system that supports the "Analects of Confucius" learning for Chinese learner and the "Analects of Confucius" learner. We present pragmatics and topic information to the learner. There are two roles of pragmatics and topic information. The first one is to clarify the verse semantic acid the second is to make "Analects of Confucius" contents familiar to the learner. The experiments evidenced the effectiveness of the presented pragmatics and topic information that is specific to increase enthusiasm for learning and enhance understanding of the contents.


Emotion Recognition Based on Negative Words and Pattern Matching For Chinese Negative Sentences

Abstract Recently, emotion recognition with computer has attracted a great deal of attention to researchers for its broad applications. Emotion estimation from textual input has also become active as natural language processing (NLP) technology develops. However, when it comes to negative sentences in Chinese, the original emotion estimation may be reversed which makes obtaining correct recognition results difficult if we do not consider the effect of negative words.


Negative Expression Translation for Japanese and Chinese Machine Translation System

There are many differences between Chinese and Japanese in history, culture, life, manners and customs, therefore it is natural that there are particular words and special usage of words in two languages. It is necessary to correctly master the meaning of these particular words and then translate accurately between two languages. Because of the complex corresponding relationship in Chinese and Japanese, when building Japanese-Chinese machine translation, it is easy to introduce vagueness. Among the mistakenly translations in existing commercial translation software, most of them are caused by the negative expression in the sentence. In this paper, through analyzing the negative expression ways in Chinese and Japanese languages, we investigated the translation of Japanese-Chinese negative sentences by using the selection rules of Chinese negative words and position rules. The basic Japanese negative expression naide/(sic) has the same meaning with mei/(sic) and bie/(sic) in Chinese. When translating nakute/(sic) into Chinese, according to its negative meaning and grammatical rule, it may be translated into bu/(sic). In the current research, we discussed the general rules which are abstracted from typical examples including naide/(sic) and nakute/(sic).


Construction of "Analects of Confucius" Knowledge Base Including Pragmatics Information

語用情報を含む「論語」知識ベースの構築

In this paper, we propose an approach of constructing the knowledge base for ``Analects of Confucius'', which aims to help the correct understanding of ``Analects of Confucius''. The content of ``Analects of Confucius'' has the characteristics that it has not been categorized by topics and always has comprehensive meanings. Thus it is necessary to build an framework to manage and build the knowledge base for it. The construction of knowledge base in the past work has been focusing on the research of the words and the shallow meaning and explicitly communicated meaning of the passages which can not be used for deeper meaning and implicitly communicated meaning. The present paper sets up an categorization system for ``Analects of Confucius'', then based on it, the knowledge base is built by using pragmatics information with reference to utterance interpretation method in pragmatics. The question answering system adopts the knowledge base of ``Analects of Confucius'' and gives an assessment of it. The results show that the combined answer with pragmatics information and modern text interpretation outperforms the answers which are extracted only from modern text by 32.2%.

本論文では，語用情報を活かして，「論語」に関する知識ベースを構築した．そして，この知識ベールを活かして，質問回答システムを開発し，実験により，「語用情報」の有効性を確認した．

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)


Statistic Information and Grammatical Features Based Emotion Recognition For Chinese Split Phrases

Emotion recognition in context is a new research issue with great significance. It can help people acquire the useful information from reviews or comments in the Internet. In Chinese context, there is a syntactic construction called split phrase that always carries emotional information, which cannot be recognized accurately by general lexical analysis. In order to find an effective way for recognizing this construction, we study the classification and calculation rules of split phrases in Chinese. In this paper, an emotion recognizing method based on the statistic information and grammatical feature is proposed and evaluated. The comparing experimental results show that the emotion recognition rate is improved efficiently.


Integration of Multiple Classifiers for Chinese Semantic Dependency Analysis

Semantic Dependency Analysis (SDA) has extensive applications in Natural Language Processing (NLP). In this paper, an integration of multiple classifiers is presented for SDA of Chinese. A Naive Bayesian Classifier, a Decision Tree and a Maximum Entropy classifier are used in a majority wins voting scheme. A portion of the Penn Chinese Treebank was manually annotated with semantic dependency structure. Then each of the three classifiers was trained on the same training data. All three of the classifiers were used to produce candidate relations for test data and the candidate relation that had the majority vote was chosen. The proposed approach achieved an accuracy of 86% in experimentation, which shows that the proposed approach is a promising one for semantic dependency analysis of Chinese.

Electronic Notes in Theoretical Computer Science


Automatic Extraction of Super-Function From Bilingual Corpus

Extraction of a large Super-Function (SF) is one of the most important factor in realizing SF based machine translation. This paper presents a method to automatically extract SF from a Japanese-English bilingual corpus. The extraction process matches Japanese noun and English noun in each bilingual sentence in a bilingual corpus using a bilingual dictionary. The experimental results show that this method performs very well in automatically extracting SF for machine translation. Then, we discuss a problem of SF based machine translation from the result of the evaluation experiment using extracted SF.


In this paper we explore some of the most important areas of advanced information retrieval. In particular, we look at cross-lingual information retrieval, multimedia information retrieval and semantic-based information retrieval. Cross-lingual information retrieval deals with asking questions in one language and retrieving documents in one or more different languages. With an increasingly globalized economy, the ability to find information in other languages is becoming a necessity. Multimedia information retrieval deals with finding media other than text, i.e. music and pictures. With the explosion of digital media that is available on the Internet and present on users' computers techniques for quickly and accurately finding desired media is important. Semantic based information retrieval goes beyond classical information retrieval and uses semantic information to understand the documents and queries in order to aid retrieval. Semantic based information retrieval goes beyond standard surface information by using the concepts represented in documents and queries to improve retrieval performance.

この招待論文では，情報検索の分野における最も重要な領域，具体的には多言語情報検索，マルチメディア情報検索および意味に基づいた情報検索について述べる．多言語情報検索は，ある言語で検索した時，自動的に他言語で記述されたドキュメントも検索対象とする方法である．マルチメディア情報検索は，テキスト以外の音楽，画像などの各種メディアを対象とした情報検索であり，個人のPCやインターネット上にこうしたメディアが溢れている現状においては重要な技術となる．意味に基づいた情報検索は従来の表層表現からの情報検索を越え，意味を概念として表現することで検索性能を向上させている．


Category Classification and Topic Discovery of Japanese and English News Articles

This paper presents algorithms for topic analysis of news articles. Topic analysis entails category classification and topic discovery and classification. Dealing with news has special requirements that standard classification approaches typically cannot handle. The algorithms proposed in this paper are able to do online training for both category and topic classification as well as discover new topics as they arise. Both algorithms are based on a keyword extraction algorithm that is applicable to any language that has basic morphological analysis tools. As such, both the category classification and topic discovery and classification algorithms can be easily used by multiple languages. Through experimentation the algorithms are shown to have high precision and recall in tests on English and Japanese.


Affective Information Processing and Recognizing Human Emotion

Information recognition and extraction of human emotions are necessary for machines to communicate smoothly with humans and to realize emotion communications. We focus on human psychological characteristics to develop general-purpose agents that can recognize human emotion and create machine emotion. We comprehensively analyze brain waves, voice sounds and picture images that represent information included in emotion elements of phonation, facial expressions, and speech usage. We analyze and estimate many statistical data based on the latest achievements of brain science and psychology in order to derive transition networks for human psychological states. We establish a speaker word model for researching computer simulation of psychological change and emotional presentation, developing emotion interface, and establishing theoretic structure and realization method of emotion communication. A new approach for recognizing human emotion based on Mental State Transition Network will be described and one emotion estimation method based on sentence pattern of emotion occurrence events will be discussed, and some new results of the project will be given.

人間の感情を推定する技術は，機械と人間が自然なコミュニケーションをとる上で重要な技術である．我々は人間の感情を理解し，自分自身の感情を創出するエージェントの開発を行っている．人間の心的な特性に着目し，脳波，音声，画像による発声中の感情要素表現，顔の表情，及び言葉使いに含まれる情報を総合的に解析し，脳科学と心理学の最新の研究成果を基に，大量の統計データを解析・推定することで，人間の心的状態遷移ネットワークを導出する．本論文では，心的状態遷移ネットワークを用いた感情識別の方法について議論した後，文章のパターンに基づいた感情識別法を提案し，最後に本プロジェクトの数々の成果について紹介する．


Developing the Method of Server Controlled Outcomes Management and Variance Analysis

This paper is to describe a way to develop the Psychoms process for mental health patient management and variance analysis system has been developed using artificial intelligence. Although it is agreed that there is a need for clinical pathway variance analysis, methods for creating a system are less well defined. The procedure and systematic process described aims to improve patients' quality of life through consistent and timely care. Ultimately, its potential influence is to assist in the improvement of quality health care services. This paper illustrates a method of outcomes management and variance analysis as the prospective development of future research.

本論文では，人工知能を使って開発されたメンタルヘルス患者管理と差異分析システムのためのPSYCHOMSプロセスを開発する方法を記述した．それは，臨床経路差異分析の必要性があることが合意されているが，システムを作成するための方法は，あまりよく定義されていない．このシステムの開発過程において，タイムリーなケアを提供することによって患者の生活の質を改善するための組織的なプロセスと手順が明確にされた．最終的に，その潜在的な影響は，質の高い医療サービスを支援することである．本論文では，今後の研究や前向きの開発などのアウトカム管理と差異分析の方法を示した．


GA, MR, FFNN, PNN & GMM based Models for Automatic Text Summarization

This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First, we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. Moreover, we use all feature parameters to train feed forward neural network (FFNN), probabilistic neural network (PNN) and Gaussian mixture model (GMM) in order to construct a text summarizer for each model. Furthermore, we use trained models by one language to test summarization performance in the other language. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 Arabic political articles and 100 English religious articles. The results of the proposed approach are promising, especially the GMM approach.

本論文では，統計的方法を用いた文章要約法の改善法を検討する．文章要約に用いる特徴量としては，例えば，文中の位置，ポジティブおよびネガティブキーワード，タイトルとの文類似度，といったものを用いた．まずは各特徴量と文章要約性能の関係について調べ，遺伝的アルゴリズムと数学的回帰モデルを用いて各特徴量の最適な重みの探索を行った．更に，ニューラルネットワークや混合ガウスモデルを用いた文章要約性能についても調べた．また，ある言語で学習されたモデルを他言語に適用させた場合の性能についても実験を行った．こうした実験はアラビア語の政治記事100文章と英語の宗教に関する記事100文章を用いて，様々な圧縮率を設定して行われた．提案された方法，特に混合ガウスモデルの有効性が確かめられた．


The Technique of Emotion Recognition Based on Electroencephalogram

We aim to develop the mechanism that is possible to sympathize with man, and we target at the thing to read feelings which man feels from the brain wave. This time, as an initial stage of the research how the subject is able to make judgments to be impressed from the brain wave is verified. Concretely, it is investigated by using electroencephalograph (EEG) that the brain is an active state when the subject own declaring to have been impressed. Three kinds of evaluation method are used for this research. One method is statistically evaluated based on the strength of potential. Other method is evaluated objective based on the place which brain waves activate. Another method is evaluated by comparing a subject's subjectivity with change of EEG. Subjects are two persons and a small number this time, and since those attributes are partial, a question remains in the justification of a result. However, it is also the fact which becomes clear from this result that a subject's impression condition can fully be judged from the activity state of brain waves.

我々は，人に同情することが可能であるメカニズムの開発を目指して，脳波から感じる人の感情を読み取る事をターゲットにしている．今回は，研究の初期段階として，被験者の脳波から感動したかどうかの判断を行うことができる方法を検証した．具体的には，脳は，対象が感動したと宣言所有アクティブ状態であることを脳波計(EEG)を用いて調べた．この研究のために3種類の評価法を使用した．一つ目の方法は，可能性の強さに基づいて統計的に評価した．二つ目の方法は，脳波がアクティブな場所に基づいて客観的に評価した．もう一つの方法は，EEGの変化に被験者の主観を比較することによって評価した．被験者は，今回二人と少数であり，それらの属性が不完全であるため，少し問題が残っている． しかし，それはまた，この結果から，被験者の印象条件が完全に脳波の活動状態で判断することができることが明らかになった事実だった．

Information : an International Interdisciplinary Journal


Extracting Date/Time Expressions in Super-Function Based Japanese-English Machine Translation

Super-Functionに基づく日英機械翻訳における日付・時間表現の抽出

Super-Function Based Machine Translation(SFBMT) which is a type of Example-Based Machine Translation has a feature which makes it possible to expand the coverage of examples by changing nouns into variables, however, there were problems extracting entire date/time expressions containing parts-of-speech other than nouns, because only nouns/numbers were changed into variables. We describe a method for extracting date/time expressions for SFBMT. SFBMT uses noun determination rules to extract nouns and a bilingual dictionary to obtain correspondence of the extracted nouns between the source and the target languages. In this method, we add a rule to extract date/time expressions and then extract date/time expressions from a Japanese-English bilingual corpus. The evaluation results shows that the precision of this method for Japanese sentences is 96.7%, with a recall of 98.2% and the precision for English sentences is 94.7%, with a recall of 92.7%.

用例ベース機械翻訳の一種であるスーパー関数による機械翻訳(SFBMT)は変数に名詞を変更することで，例の範囲を拡大することを可能にする機能を持っているが，日付/時刻を含む表現を抽出する問題があった．本論文では，SFBMTの日付/時刻表現を抽出するための方法を提案した． SFBMTは，ソースとターゲット言語の間で抽出された名詞の対応を得るために，名詞と対訳辞書を抽出するために，名詞決定ルールを使用している．この方法では，日付/時刻の表現を抽出し，日本語·英語のバイリンガルコーパスから日付/時刻表現を抽出するためのルールを追加する．実験により提案した手法の有効性を確かめた．

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)


Sentence Alignment based on the Text Length between Punctuation Marks

Sentence alignment based on the text length between punctuation marks | sentence alignment; English/Arabic parallel corpus; parallel corpora; machine translation | Parallel corpora have become an essential resource for work in multi lingual natural language processing. Sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on the text character length between successive punctuation marks. A probabilistic score is assigned to each proposed correspondence of texts, based on the scaled difference of lengths of the two texts (in characters) and the variance of this difference. Using this score, the time required for punctuation marks matching decreased and the sentence alignment accuracy increased. Using this new approach, we could achieve an error reduction of 26.5% over length based approach when applied on English-Arabic parallel documents. The sentence alignment execution time decreased to 17% of the total time required for the combined model which uses length based approach and punctuation approach combined together. Moreover, the proposed approach result outperforms Melamed and Moore's approach results.


Evaluation of Emotion Estimation Methods Based on Statistic Features of Emotion Tagged Corpus

Development of information technology has been increasing the chance of interaction between human and computer. Computer used to be a tool and its development had been mainly emphasized on the speed of information processing. However, by understanding not only semantic information but also ambiguous information such as emotion, computer can be a versatile partner for human that can detect emotional state of a person and can response appropriately the emotion. As a primary study of text-based emotion estimation by computer we focused on statistically analyzing the relationship between word emotion and sentence emotion based on the originally created emotion tagged corpus then propose two emotion estimation methods according to the obtained statistic features. The proposed methods are evaluated using a prototype system.

本研究では，発話文にタグ付与をおこなったコーパスを用いた感情推定手法の評価方法を提案する．従来，機械学習を用いた統計的な感情推定手法が提案されてきたが，それらの評価方法については検討されることが少なかった．本研究では，構築した感情タグ付きコーパスを用いて，統計的な特徴を得ることで，2種類の感情推定手法を提案する．プロトタイプを用いた評価実験の結果，提案した手法が有効であることを示した．

International Journal of Innovative Computing, Information and Control


English-Arabic Proper Noun Transliteration Pairs Creation

Proper nouns may be considered the most important query words in information retrieval. If the two languages use the same alphabet, the same proper nouns can be found in either language. However, if the two languages use different alphabets, the names must be transliterated. Short vowels are not usually marked on Arabic words in almost all Arabic documents (except very important documents like the Muslim and Christian holy books). Moreover, most Arabic words have a syllable consisting of a consonant-vowel combination (CV), which means that most Arabic words contain a short or long vowel between two successive consonant letters. That makes it difficult to create English-Arabic transliteration pairs, since some English letters may not be matched with any romanized Arabic letter. In the present study, we present different approaches for extraction of transliteration proper-noun pairs from parallel corpora based on different similarity measures between the English and romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the new approaches presented using two different English-Arabic parallel corpora. Most of our results outperform previously published results in terms of precision, recall, and F-Measure.

Journal of the American Society for Information Science and Technology


Automatic Super-function Extraction for Translation of Spoken Dialogue

Extraction of a large number of Super-Function (SF) is the most importantfactor in realizing SF based machine translation. This paper presents a method for automatic extraction of SF from a Japanese-English bilingual corpus. The extraction processuses a bilingual dictionary to match Japanese and English nouns in each sentence pair.The experimental results using a Japanese-English bilingual corpus show that this methodperforms very well in automatically extracting SF for machine translation. In addition,we evaluate the extracted SF in SF based machine translation.


Chinese-Japanese Translation of Causative Sentences Using Super-function Based Machine Translation System

Causative sentences in Japanese are a matter of affixation. In particular,the causative form takes the shape of a suffix. In Chinese, the causative form constitutes an independent word. In our previous studies on Super-Function Based MachineTranslation (SFBMT), we have found that causative sentences are very frequently usedand difficult to translate correctly, the over use of causative sentences can be dangerousas it may introduce ambiguity in the translation. In this paper, we discuss the challenges in handling Japanese causative sentences in an SFBMT system; we present ashallow method for translating causative sentences by using some fixed rules and SuperFunctions (SF). In the present research, sufficient Chinese-Japanese causative sentencepatterns have been employed as a language-database for experiments, which proves thesuggested method can effectively improve translation quality within the range under discussion.


Single Document Keyword Extraction For Internet News Articles

Keywords are a fundamental part of information retrieval (IR) and as suchthey have been studied extensively. They are used for everything from searching to describing a document. A Keyword extraction algorithm can be defined as a combinationof a keyword representation and a selection/weighting scheme. The most common selection/weighting schemes are based on collection statistics or using supervised machinelearning algorithms. In these cases, keywords can, typically, only be extracted from documents that belong to a collection or using a large amount of annotated training data. Theimportance of extracting keywords without a document collection has been gradually increasing due to the Internet. In this paper, a keyword extraction algorithm designed withnews in mind that requires neither a document collection or training data is presented.It uses noun phrases as its keyword representation and takes in document statistics toderive its weighting scheme. Through experimentation it is shown that the quality of thekeywords extracted from the proposed algorithm are better than standard algorithms forboth information retrieval and humans.


The Creation of a Chinese Emotion Ontology Based on HowNet

Full comprehension of language comes about by understanding the meaning and the emotion behind the communication. Understanding the meaning of language is the goal of natural language processing and research on semantic analysis. Understanding emotion is one of the goals of affective computing. The two areas of artificial intelligence have recently come together for understanding emotion in text. In order to help in this pursuit, this paper describes a Chinese emotion ontology based on HowNet and its construction. The ontology should go a long way in helping to understand, classifiy, and recognize emotion in Chinese. The ontology created in this paper is made up of Chinese emotion predicates that can help in understanding the emotion of the actors in sentences. The ontology was semi-automatically created using a simple three step process. The final ontology has just under 5,500 verb predicates covering 113 different emotion categories.


A Low Cost Machine Translation Method for Cross-Lingual Information Retrieval

In one form or another language translation is a necessary part of cross-lingual information retrieval systems. Often times this is accomplished using machine translation systems. However, machine translation systems offer low quality for their high costs. This paper proposes a machine translation method that is low cost while improving translation quality. This is done by utilizing multiple web based translation services to negate the high cost of translation. A best translation is chosen from the candidates using either consensus translation selection or statistical analysis. Which to use is determined by a heuristic rule that takes into account that most web based translation services are of similar quality and that machine translation still produces relatively poor results. By choosing the best translation the method is able to increase translation quality over the base systems, which is verified by the experimentation.


Japanese Emotion Corpus Analysis and its Use for Automatic Emotion Word Identification

In this paper, the creation of a Japanese emotion cor- pus and its use in automatic emotion word identifcation are examined. The corpus was created by manually tagging words in just under 1,200 dialog sentences with emotion. Using the tagged corpus, statistical anal- ysis was performed to determine the characteristics of emotional expression in Japanese dialog. This type of analysis should prove benefcial for understanding how emotion is expressed and how to identify, classify, etc. emotion in Japanese. To test this theory an automatic emotion word identifcation system was built using machine learning based classifers with features taken from the statistical analysis. In total, four diferent classifers were trained and compared to a baseline dictionary approach. It was found that classifer based identifcation was able to signifcantly increase recall.


This work proposes an approach to address automatic text summarization. This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features score function to train genetic algorithm (GA) and mathematical regression (MR) models to obtain a suitable combination of feature weights. The proposed approach performance is measured at several compression rates on a data corpus composed of 100 English religious articles. The results of the proposed approach are promising.

International Journal of Computer Science


Empathetic Understanding as Caring in Nursing Using Electroencephalographic Data as Evidence

We presume that the measurement of electroencephalographic (EEG) changes, those activities that are considered physiological indicators, enables an objective understanding of changes in emotions of those who have difficulty in expressing these through facial expression or physical action. Generally, EEG is used in the hospital to examine encephalopathy and brain disorder. Using an electroencephalograph device to acquire digital data we propose a method to objectively capture changes in the recognition state of people from changes in EEG activities (action potential), and a way to apply it into a clinical situation.


Improving Parsing of BA Sentences for Machine Translation

The research on Chinese-Japanese machine translation has been lasting for many years, and now this research field is increasingly thoroughly refined. In practical machine translation system, the processing of a simple and short Chinese sentence has somewhat good results. However, the translation of complex long Chinese sentence still has difficulties. For example, these systems are still unable to solve the translation problem of complex BA sentences. In this article a new method of parsing of BA sentence for machine translation based on valency theory is proposed. A BA sentence is one that has a prepositional word BA. The structural character of a BA sentence is that the original verb is behind the object word. The object word after the BA preposition is used as an adverbial modifier of an active word. First, a large number of grammar items from Chinese grammar books are collected, and some elementary judgment rules are set by classifying and including the collected grammar items. Then, these judgment rules are put into use in actual Chinese language and are modified by checking their results instantly. Rules are checked and modified by using the statistical information from an actual corpus. Then, a five-segment model used for BA sentence translation is brought forward after the above mentioned analysis. Finally, we applied this proposed model into our developed machine translation system and evaluated the experimental results. It achieved a 91.3% rate of accuracy and the satisfying result verified effectiveness of our five-segment model for BA sentence translation.

実用的な機械翻訳システムでは，簡単かつ短い中国語文の処理が若干良好な結果を有する．しかし，複雑な長い中国語文章の翻訳はまだ問題を抱えている．例えば，これらのシステムは，依然として複雑なBA文の翻訳の問題を解決できない．この論文ではBA文の構文解析の新しい方法が提案されている．まず，中国語文法書から文法項目が多数集められ，いくつかの基本の判定ルールを分類し，収集された文法項目を含めることによって設定される．そして，これらの判定ルールが実際中国語解析で実用化されており，その結果を確認することにより修飾される．ルールがチェックされてから実際のコーパスから統計情報を使用して変更される．提案されたモデルを適用し，実験結果を評価した．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


The Framework of Mental State Transition Analysis

The Human Computer Interaction (HCI) Technology has emerged in the different fields in applications in computer vision and recognition systems such as virtual environment, video games, e-business and multimedia management. In this paper we propose a framework of designing the Mental State Transition (MST) of a human being or virtual character. The expressions of human emotion can be easily remarked by facial expressions, gestures, sound and other visual characteristics. But the potential MST modeling in affective data are always hidden actually. We analysis the framework of MST and employ DBNs to construct the MST networks and finally the experiment has been implemented to derive the ground truth of the data and verify the effectiveness.


Emotion Estimation Algorithm Based on Interpersonal Emotion Included in Emotional Dialogue Sentences

Emotion recognition aims to make computer understand ambiguous information of human emotion. Recently, research of emotion recognition is actively progressing in various fields such as natural language processing, speech signal processing, image data processing or brain wave analysis. We propose a method to recognize emotion in dialogue text by using originally created Emotion Word Dictionary. The words in the dictionary are weighted according to the occurrence rates in the existing emotion expression dictionary. We also propose a method to judge the object of emotion and emotion expressivity in dialogue sentences. The experiment using 1,190 sentences proved about 80% accuracy.

感情推定は，人間のあいまいな情報である感情をコンピュータに理解させるために必要である．言語・音声・画像などの情報から，人の感情を認識することが，近年のアフェクティブコンピューティングにおける最重要課題である．対話における感情理解のためには，少なくとも1名以上の相手がいる会話において生起する感情に着目する必要がある．本研究では，1190文の発話文を対象として，対人関係に着目し，話者が自分のことをどう呼ぶか，他社のことをどう呼んでいるかという点に基づく感情推定手法を提案する．


Color Features Based Speaking Detection with Hidden Markov Model in Video Sequences

The Human Computer Interface Technology has faced challenges of understanding user's mind actively. In the ¯rst, the speak detection is a primary technique in applications of human computer interface(HCI) and other applications like surveillance system, video conferenceand multimedia data base management in computer vision and speechrecognition. This paper describes a novel method to detect speaker witha probabilistic model of behavior of speaking. After human face recognition, the especial components under the nonlinear transformation incolor space of lip represent the speci¯c mouth region and then combine the groups of coherent motions . Next the simple movements in themouth region are modeled by hidden Markov models. The experimentalresults demonstrate that the model representing speaking is e±ciencyand successful in applying to driver video surveillance system.


A Construction of Emotion Thesaurus Basing on Chinese Character and Empirical Knowledge

There have been some studies about spoken natural language dialog, and most of them have successfully been developed within the specified domains. However, current human-computer interfaces only get the data to process their programs. Aiming at developing an affective dialog system, we have been exploring how to incorporate emotional aspects of dialog into existing dialog processing techniques. As a preliminary step toward this goal, we work on making a Chinese emotion classification model which is used to recognize the main affective attribute from a sentence or a text. Finally we have done experiments to evaluate our model.


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.

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

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


Speaker Identification Method Using Earth Mover's Distance for CCC Speaker Recognition Evaluation 2006

In this paper, we present a non-parametric speaker identification method using Earth Movers Distance (EMD) designed for text-indepedent speaker identification and its evaluation results for CCC Speaker Recognition Evaluation 2006, organized by the Chinese Corpus Consortium (CCC) for the th International Symposium on Chinese Spoken Language Processing (ISCSLP 2006). EMD based speaker identification (EMD-IR) was originally designed to be applied to a distributed speaker identification system, in which the feature vectors are compressed by vector quantization at a terminal and sent to a server that executes a pattern matching process. In this structure, we had to train speaker models using quantized data, then we utilized a non-parametric speaker model and EMD. From the experimental results on a Japanese speech corpus, EMD-IR showed higher robustness to the quantized data than the conventional GMM technique. Moreover, it achieved higher accuracy than GMM even if the data was not quantized. Hence, we have taken the challenge of CCC Speaker Recognition Evaluation 2006 using EMD-IR. Since the identification tasks defined in the evaluation were on an open-set basis, we introduce a new speaker verification module. Evaluation results show that EMD-IR achieves 99.3 % Identification Correctness Rate in a closed-channel speaker identification task.

International Journal of Computational Linguistics & Chinese Language Processing


An Emotion Recognition Conversation System Based on Knowledge Database Automatic Architecture

As conversation system it must converse with various users and have to talk about various conversations, so we have to prepare large numbers of information for conversation knowledge database, but it is so especially difficult for us. The wealth of information on the web is full and quick so those make it an attractive resource for seeking quick information to simple. Recognizing a human's emotional state can be helpful in various contexts. The most promising one is probably the man-machine interaction, the communication between an assisting robot in the household and its human user. In this paper we present an experiment named an emotion recognition conversation system based on knowledge database automatic architecture. In this exploration there are two parts different than the generic conversation system. First, our conversation system can automatic construct of knowledge database base on the Really Simple Syndication parse. Second, this system can recognize emotion from the conversation contents. The experiment showed that this method could achieve better results in practice.

Communications in Computer and Information Science


Chinese semantic dependency analysis: Construction of a treebank and its use in classification

Semantic analysis is a standard tool in the Natural Language Processing (NLP) toolbox with widespread applications. In this article, we look at tagging part of the Penn Chinese Treebank with semantic dependency. Then we take this tagged data to train a maximum entropy classifier to label the semantic relations between headwords and dependents to perform semantic analysis on Chinese sentences. The classifier was able to achieve an accuracy of over 84%. We then analyze the errors in classification to determine the problems and possible solutions for this type of semantic analysis.

セマンティック分析は，広範なアプリケーションとの自然言語処理の重要なツールである．この論文では，意味的依存関係とPenn中国語ツリーバンクの一部をタグ付けを行った．中国語文章に意味解析を実行するために見出し語と係り受け間の意味関係にラベルを付ける最大エントロピー分類器を訓練するために，データタグ付けを取った．分類器は84%の精度を達成することができた．

ACM Transactions on Speech and Language Processing


Parallel corpora have become an essential resource for work in multilingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross-language information retrieval and machine translation applications. In this paper, we present two new approaches to align EnglishArabic sentences in bilingual parallel corpora based on probabilistic neural network (P-NNT) and Gaussian mixture model (GMM) classifiers. A feature vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually prepared training data was assigned to train the probabilistic neural network and Gaussian mixture model. Another set of data was used for testing. Using the probabilistic neural network and Gaussian mixture model approaches, we could achieve error reduction of 27% and 50%, respectively, over the length based approach when applied on a set of parallel EnglishArabic documents. In addition, the results of (P-NNT) and (GMM) outperform the results of the combined model which exploits length, punctuation and cognates in a dynamic framework. The GMM approach outperforms Melamed and Moores approaches too. Moreover these new approaches are valid for any languages pair and are quite flexible since the feature vector may contain more, less or different features, such as a lexical matching feature and Hanzi characters in JapaneseChinese texts, than the ones used in the current research.

パラレルコーパスは，多言語自然言語処理に不可欠な資源となっているが，．しかし，文アラインメントが重要な役割になり，文アラインメントを行う効率的手法が要求されている．本稿では，確率的ニューラルネットワーク(P-NNT)とガウス混合モデル(GMM)の分類に基づいて，バイリンガルパラレルコーパスに英語・アラビア語文を整列させるために二つの新しいアプローチを提案した．いろいろな実験を行って，提案したモデルの効率性を確認した．


A Model of Mental State Transition Network

Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly. Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

感情は，人間の知性の最も本質的かつ基本的な属性の1つである．人工心理学のモデルに関する研究は，人間とコンピュータの相互作用の開発の最初のステップである．本論文では人間の感情を検出するため心状態遷移ネットワークモデルを提案した．心理的な一連の実験により，様々な刺激下で，感情の状態に応じて，人間の感情の遷移確率を求める．7相対評価実験を比較すると，提案されたモデルのためのサンプルのセットを使用して0.843の平均適合率が達成された．

IEEJ Transactions on Electronics, Information and Systems


Emotion Estimation Algorithm based on Emotion Occurrence Sentence Pattern

感情生起事象文型パターンに基づいた会話文からの感情推定手法

近年の情報処理技術の発達に伴い, 従来の情報処理の分野ではほとんど取り扱われなかった人間の感性をコンピュータで処理しようとする試みが盛んになってきた. 擬人化エージェントや感性ロボットが人のように振舞うためには, 人間が表出する感情を認識し, 自ら感情を表出することが必要である. 我々は, 感性ロボットに応用するための感情認識技術について研究している. 自然言語会話文からの感情推定を行う試みは, 多くの場合, 表面的な感情表現のみに絞つて行われてきた. しかし, 人間の発話時には常に何らかの感情が含まれていると考えられる. そこで, 本稿では, 感情語と感情生起事象文型パターンに基づいた感情推定手法を提案し, 実験システムを構築する. そして, 本手法の有効性を調べるため, シナリオ文を対象にその評価実験を行った.


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．

Transactions of Information Processing Society of Japan


Mining News Sites to Create Special Domain News Collections

AbstractWe present a method to create special domain collections from news sites. The method only requires a single sample article as a seed. No prior corpus statistics are needed and the method is applicable to multiple languages. We examine various similarity measures and the creation of document collections for English and Japanese. The main contributions are as follows. First, the algorithm can build special domain collections from as little as one sample document. Second, unlike other algorithms it does not require a second general corpus to compute statistics. Third, in our testing the algorithm outperformed others in creating collections made up of highly relevant articles.

International Journal of Computational Intelligence


Wind noise reduction method using the observed spectrum fine structure and esimated spectrum envelope

スペクトルの微細構造を考慮した風雑音除去手法

本論文では，パワーやスペクトル形状の変化の大きい風雑音を高精度で推定する 手法を提案する． 提案手法は，複数の参照用風雑音スペクトル包絡を用いフレーム毎に 風雑音のスペクトル包絡を 推定すると共に，当該フレームから得られる観測信号の微細構造を用い 風雑音スペクトルを推定し雑音除去を行う． 提案手法の評価を行うため，扇風機により発生させた風雑音を用いたシミュレー ション実験を実施した． 客観評価実験としてセグメンタル SNRによる評価 を行った結果，提案手法はスペクトルサブトラクション(SS)法よりも大幅なセグメンタルSNRの 改善を達成した．また，CMOSによる主観評価実験を行った結果，客観評価と同様に SS法と比較して大幅な改善を達成し，提案手法の有効性が示された． また実験に用いたデータを調べた結果，提案手法を用いれば， 推定スペクトルと実際の雑音スペクトル間のミスマッチを低減することができ， ミュージカルノイズと呼ばれる人工的な雑音の抑制にも有効であることが分かった．

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

電子情報通信学会論文誌(A)


Extracting Chinese Idiomatic Expressions from Texts to Author Reading Support Systems for Learning Chinese as Second Language

Though most current researches are focusing on Chinese reading support, there is still no system that can provide a convenient support aiming at CIE (Chinese Idiomatic Expression). In this paper, we mainly propose how to help CSL (Chinese as Second Language) learners to recognize CIE in Chinese texts by using NLP (Natural Language Processing) technique. We have created a CIE database with 2,305 idiomatic expressions of contemporary Chinese. The recognition processing of CIE consists of the CIE registration, the lexical analysis, the classification of sentences, the correction of POS tags and the extraction of CIE. In our experiment of CIE recognition, we have achieved 81.65% in Recall and 94.34% in precision. According to related researches of CIE recognition and comprehension, we created a RDBMS of CIE compre-hension support resources. For helping the learners to comprehend CIEs efficiently, we have designed a Support Program which connects the results of CIE recognition and the CIE comprehension support resources.

The Journal of Information and Systems in Education


Sentence Alignment using Feed Forward Neural Network

Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than non-aligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to align sentences in bilingual parallel corpora based on feed forward neural network classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuate score, and cognate score values. A set of manually prepared training data has been assigned to train the feed forward neural network. Another set of data was used for testing. Using this new approach, we could achieve an error reduction of 60% over length based approach when applied on English Arabic parallel documents. Moreover this new approach is valid for any language pair and it is quite flexible approach since the feature parameter vector may contain more/less or different features than that we used in our system such as lexical match feature.

本論文では，フィードフォワードニューラルネットワーク分類器に基づくバイリンガルパラレルコーパスで文章を整列させるために新しいアプローチを提案した．特徴量ベクトルはテキスト対から抽出される．このベクターは，テキストの長さなどの機能，同族スコア値が含まれている．手動で調製されたトレーニングデータのセットは，フィードフォワードニューラルネットワークを訓練するために割り当てられている．データの別のセットは，試験に使用した．英語アラビア平行文書に適用された場合，この新しいアプローチを使用して，長さに基づくアプローチより60%の誤差低減を達成できた．


Evaluation of EMD-based Speaker Recognition using ISCSLP2006 Chinese Speaker Recognition Evaluation Corpus

In this paper, we present the evaluation results of our proposed text-independent speaker recognition method based on the Earth Mover's Distance (EMD) using ISCSLP2006 Chinese speaker recognition evaluation corpus developed by the Chinese Corpus Consortium (CCC). The EMD based speaker recognition (EMD-SR) was originally designed to apply to a distributed speaker identification system, in which the feature vectors are compressed by vector quantization at a terminal and sent to a server that executes a pattern matching process. In this structure, we had to train speaker models using quantized data, so that we utilized a non-parametric speaker model and EMD. From the experimental results on a Japanese speech corpus, EMD-SR showed higher robustness to the quantized data than the conventional GMM technique. More- over, it has achieved higher accuracy than the GMM even if the data were not quantized. Hence, we have taken the challenge of ISCSLP2006 speaker recognition evaluation by using EMD-SR. Since the identification tasks were on an open-set basis, we introduce a new speaker verication module in this paper. Evaluation results showed that EMD-SR achieves 99.3% Identification Correctness Rate in a closed-channel speaker identication task.


Chinese Text Segmentation System Using Sensitive Word Concept

The identification of words in Indo-European languages is a trivial task. However, the problem named text segmentation has been, and is still a bottleneck for various Asian languages, such as Chinese. There have been two main groups of approaches to Chinese segmentation: dictionary-based approaches and statistical approaches. However, both approaches have difficulty to deal with some Chinese texts. To address the difficulties, we propose a hybrid approach using Sensitive Word Concept to Chinese text segmentation. Sensitive words are the compound words whose syntactic category is different from those of their components. According to the segmentation, a sensitive word may play different roles, leading to significantly different syntactic structures. In this paper, we explain the concept of sensitive words and their efficacy in text segmentation firstly, then describe the hybrid approach that combines the rule-based method and the probability-based method using the concept of sensitive words. Our experimental results showed that the presented approach is able to address the text segmentation problems effectively.

Journal of Asian Information-Science-Life


Machine-Aided English Writing Function in MMM Projest

Multi-lingual Multi-function Multi-media intelligent system (MMM) is a big project. It is a complex intelligent system with multiple functions that can deal with multiple languages and multiple media. We have designed a new general ontology based natural language processing system referred to as Multi-lingual Multi-function Multi-media Intelligent System. This paper describes a new Machine-Aided Writing Function in the MMM. The methodology proposed in this paper can deal with any foreign language, but we focus only on how to construct a Machine-Aided English Writing system for Japanese users. With the rapid development of the internet, writing English becomes daily work for computer users all over the world. However, for most non-native users, writing English is a big challenge. To build a machine-aided system that helps non-native users not only on spelling checking and grammar checking but also on producing accurate English expressions is a challenging task. The basic idea of the method proposed in this paper is based on Super-Function (SF) means and Corpora Intelligence Technique (CIT). SF is a new concept that we present to develop multi-lingual machine translation. A prototype Machine Aiding English Writing system has been constructed based on the proposed method. Some experiments using the prototype system have been carried out and the results show the proposed method is effective. This paper discusses the SF and CIT in detail and give some new advances in the MMM project.


An Experimental parallel machine translation system

Many machine translation systems have been developed based on different machine translation approaches. The accuracy of machine translation can be further improved by integrating different machine translation systems into one system using parallel processing techniques. We have designed and implemented an experimental parallel machine translation (PMT) system. The system consists of four independent machine translation subsystems. Each subsystem is implemented using an existing machine translation technique and has its own characteristics. When translating a sentence, each subsystem translates it independently. If more than one subsystem translates the sentence successfully, the controller chooses the best translation according to a combining algorithm implemented using statistics collected from practical translations. If no subsystem succeeds in translating the sentence, the controller partitions the sentence into sentential parts, coordinates the subsystems to translates the sentential parts, and combines the partial translation results into a translation for the whole input sentence. In this paper, we describe our experimental PMT system, present some experimental results, and outline some future research on PMT.


Mechanism Approach that Unifies AI, and AI with AE

Both Artificial Intelligence (AI) and Artificial Emotion (AE) have received much attention from academic as well as engineering circles. Structuralism, functionalism and behaviorism have been the three dominant approaches to the simulation of intelligence in history up to the present. All the approaches have made progress so far whereas they also, however, facing difficulties in their development. Further more, there have been no connections between the research in AI and that in AE. An attempt was thus made in the paper to propose another approach to the research in AI and AE. Different from the structuralism, functionalism and behaviorism, this approach is featured with the direct concern with the central mechanism of intelligence and emotion and is therefore termed mechanism approach. It is discovered as consequence that the transformations from information to knowledge and further to intelligence are the nucleus of the mechanism to AI and AE. A by-product that seems significant also achieved the same time that the three named approaches to AI can well be unified within the framework of mechanism approach. These discoveries, both AI and AE can successfully be simulated by mechanism approach and unifying the three approaches into harmonious one, may open up a new stage for the research in AI and AE as well as the integration of these two.


Emotion Estimation System based on Emotion Occurrence Sentence Pattern

The approach of emotion estimation from the conventional text was for estimating superficial emotion expression mainly. However emotions may be included in humans utterance even if emotion expressions are not in it. In this paper, we proposed an emotion estimation algorithm for conversation sentence. We gave the rules of emotion occurrence to 1616 sentence patterns. In addition, we developed a dictionary which consisted of emotional words and emotional idioms. The proposed method can estimate emotions in a sentence by matching the sentence pattern of emotion occurrence and the rule. Furthermore, we can get two or more emotions included in the sentence by calculating emotion parameter. We constructed the experiment system based on the proposed method for evaluation. We analyzed weblog data including 253 sentences by the system, and conducted the experiment to evaluate emotion estimation accuracy. As a result, we obtained the estimation accuracy of about 60 %.

本研究では，感情が生起する事象について記述した文のパターンと感情パラメータとの対応とを付与したデータベースを構築し，ウェブログデータに対して感情推定をおこなう手法を提案する．本提案手法により，約60%の精度の感情推定精度が得られた．


Treatment of Quantifiers in Chinese-Japanese Machine Translation

Quantifiers and numerals often cause mistakes in Chinese-Japanese machine translation. In this paper, an approach is proposed based on the syntactic features after classification. Using the difference in type and position of quantifiers between Chinese and Japanese, quantifier translation rules were acquired. Evaluation was conducted using the acquired translation rules. Finally, the adaptability of the experimental data was verified and the methods achieved the accuracy of 90.75%, which showed that they were effective in processing quantifiers and numerals.


A Semantic Analyzer for Aiding Emotion Recognition in Chinese

In this paper we present a semantic analyzer for aiding emotion recognition in Chinese. The analyzer uses a decision tree to assign semantic dependency relations between headwords and modifiers. It is able to achieve an accuracy of 83.5%. The semantic information is combined with rules for Chinese verbs containing emotion to describe the emotion of the people in the sentence. The rules give information on how to assign emotion to agents, receivers, etc. depending on the verb in the sentence.


Text-Based English-Arabic Sentence Alignment

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on the use of the linguistic information of the text pair in Gaussian mixture model (GMM) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, cognate score and a bilingual lexicon extracted from the parallel corpus under consideration. A set of manually prepared training data has been assigned to train the Gaussian mixture model. Another set of data was used for testing. Using the Gaussian mixture model approach, we could achieve error reduction of 160% over length based approach when applied on English-Arabic parallel documents. In addition, the results of (GMM) outperform the results of the combined model which exploits length, punctuation, cognate and bilingual lexicon in a dynamic framework.


A Rule and Super Function-based Machine Translation System for Chinese-Japanese Causative Sentences

Machine translation (MT) can be generally classified as Rule-Based Machine Translation (RBMT) or Example-Based Machine Translation (EBMT) or Statistics-Based Machine Translation (SBMT), and each has numerous problems which remain unsolved. A rule-based machine translation system is an effective way to implement a machine translation system because of its extensibility and maintainability. However, it is generally disadvantageous in processing efficiency. In this paper, we present a hybrid approach based on fixed rules and Super Functions (SF) - Based method. SF is known for its lower costs, and is applicability to common users who do not have high demand on translation quality. Our purpose is to improve the processing efficiency and the quality of machine translation. In the present research, sufficient Chinese-Japanese causative sentence patterns have been employed as a language-database for experiment, which proves the suggested method can effectively improve translation quality within the range under discussion.

機械翻訳(MT)は，一般的にルールベースの機械翻訳(RBMT)または実例ベースの機械翻訳(EBMT)または統計ベースの機械翻訳(SBMT)として分類することができ，それぞれが未解決のままに多くの問題を持っている．ルールベースの機械翻訳システムは，その拡張性と保守性のため，機械翻訳システムを実現するための効果的な方法である．しかし，処理効率は一般的に不利である．本稿では，固定ルールやスーパー関数(SF)ベースに基づき，ハイブリッド手法を提示した．SFは，コストが低いことや，翻訳の品質に高い需要を持っていない一般的なユーザーにも適用可能という特徴に知られている．我々の目的は，機械翻訳の処理効率と品質を改善することである．本研究では，言語データベースとして十分な中日使役文パターンは採用された実験は，この提案手法が効果的に翻訳論じ範囲の品質を向上させることができることを証明した．


The Validity of Metaphor in Emotion Recognizing Model

Natural language has attracted importance to some researchers working on Emotion recognizing. We have made a Chinese emotion classification model and this time we add a new element to the emotion thesaurus of this model-metaphor. Through the studying of emotion, we find the metaphor is an important way of people's communication in natural language. In this paper, firstly, we introduce every part of our emotion classification model and are focus on the new element of metaphor in our emotion thesaurus. And then we do comparative experiments in order to test our model using metaphor before and after. At last the evaluation of our model is given and our model improved through using metaphor is proved to be a more effective model in recognizing emotion from text.

自然言語は，感情認識が重要な一部分ため，いくつかの研究者を集めている．我々は，中国語の感情分類モデルを作った上，今回は，このモデル-メタファーの感情シソーラスに新しい要素を追加した．感情の研究を通じて，我々は自然言語でメタファーが人々のコミュニケーションの重要な手段であることを見つけた．本論文では，まず，我々はこの感情分類モデルの各部分を紹介し，我々の感情シソーラスにおけるメタファーの新しい要素に焦点として紹介した．そして，我々はメタファーを使わないと使うの比較実験を行い，モデルをテストした．最後に我々のモデルの評価が与えられ，そのモデルは，メタファーの利用を通して改善されたことにより，テキストから感情を認識することに，より有効なモデルであることが証明された．


Rule-based Translation of Quantifiers for Chinese-Japanese Machine Translation

Quantifiers and numerals often cause mistakes in Chinese-Japanese machine translation. In this paper, an approach to quantifier translation is proposed based on the syntactic features after classification. First, morphological analysis is performed on sentences extracted from a Chinese-Japanese aligned corpus, which consists of quantifiers and numerals. Next, statistical information is obtained based on the meaning of the nouns with an accompanying quantifier. Using the difference in type and position of quantifiers between Chinese and Japanese, quantifier translation rules were acquired. Evaluation was conducted by using the acquired translation rules. Finally, the adaptability of the experimental data was verified and the methods achieved an accuracy of 90.75%, showing that they were effective in processing quantifiers and numerals translation.

中日機械翻訳で，数量詞と数字は頻繁に間違いを起こしている．本論文では，分類後に構文上の特徴に基づいて数量詞の翻訳へのアプローチを提案した．まず，数量詞と数字から構成された中国語-日本語の整列コーパスから抽出された文に対して形態素解析を実行した．次に，添付の数量詞と名詞の意味に基づき，統計情報を求めた．タイプと中国語-日本語の数量詞の位置の違いを利用し，数量詞の翻訳ルールが獲得した．取得した翻訳ルールを用いて評価を行った． 最後に，実験データの適合性が確認され，この方法が90.75%の精度を達成したことで，このような翻訳ルールは数量詞と数字の翻訳に有効であったことを示した．


An Integrated System for Semantic Analysis of Chinese

Semantic dependency analysis has extensive applications in Natural Language Processing (NLP). In this paper, we present an integrated system for semantic dependency analysis of Chinese. The system is composed of 3 main modules; Syntactic analysis, headword assignment, and semantic dependency assignment. For the semantic dependency module, many classifiers are tested. Primarily, we focus on assigning headwords and determining semantic relations between headwords and modifiers. For headword assignment, we handcrafted a set of rules and achieved a very high precision of 99.54%. For determining semantic relations, we experiment with four supervised machine learning algorithms with varying features for Headword-Modifier relations assignment, and achieved precision of 84%. The experimental results show that our approaches are an effective step in developing a semantic dependency analyzer for Chinese.

意味依存解析は自然言語処理の分野で広範囲に応用されている．本文では，我々は中国語の意味依存解析の統合システムを提案した．このシステムは，主に統語解析，見出語割当と意味依存割当，三つのモジュールに構成されている．意味依存モジュールでは，多数の分類器にテストを行った．我々は主に見出語の割当と見出語と修飾語の意味関係の定義に注目した．見出語の割当では，我々は一連のルールの作成に手作業をし，99.54%の高精度を得た．意味関係の定義では，我々は，四つの，見出語--修飾語関係を割り当てる様々な特徴量を持つ教師あり機械学習アルゴリズムをテストし，84%の精度を得た．実験結果は，我々の方法が中国語意味依存解析器の開発に効果的な一歩を踏み出したと示した．


Building Frames of Knowledge for Causal Agents in WordNet

WordNet has become a standard tool in the NLP researcher's toolkit. While giving a plethora of information it does lack certain information that would be of great benefit. This paper examines building frames of knowledge for a subset of causal agents in WordNet. This extra knowledge can help in Question & Answering, Machine Translation, etc. After an examination of the WordNet glosses different classes were created that allow for obtaining knowledge about actions, attributes, and domains.

WordNetは自然言語処理の研究者達の標準ツールとして使用されている．だが過剰な情報が与えられた時，重要な価値を持つ特定の情報を見逃すことがある．本文ではWordNetの因果エージェントの部分集合のための知識フレームの構築を試した．この知識フレームは問答システム，機械翻訳などに役に立てると予測する．WordNetの註解にテストすることで，様々なクラスを作成し，動作，属性，領域に関わる知識を得ることができた．


An Interactive E-learning System for Practicing Team Care by Interdisciplinary Collaboration

"In recent years, remote education systems using telecommunication tools such as television and the internet have been developed and applied not only in open universities and preparatory schools, but also in areas where educational resources are scarce. However, these systems do not make immediate responses to questions from the learners in real time. In addition, an interactive education system connecting the computer system and the learners who need to practice team care by interdisciplinary collaboration has yet to be developed. That is the reason why we present ""interactive e-learning system for practicing team care by interdisciplinary collaboration"" and explore the possibility of introducing such a system into practice. As regards future considerations, it is important to develop effective learning content for this system. This content promotes and reinforces the essential attitude and skills for practicing interdisciplinary team care. Determination of the effectiveness of its application depends on whether the learners can practice interdisciplinary team care virtually and apply their learning to real situations."


Non-Verbal Voice Emotion Analysis System

A non-verbal voice analysis system that recognizes, separates and ranks concurrent emotions in real time has potential application in various fields, yet such a system that could delineate emotion based solely on the sound of human voice has not been successfully demonstrated before. Here, we propose a system that recognizes human emotion by means of analyzing the fundamental frequency of human voice taken from continuous natural speech. The system detects robust fundamental frequencies and intonations by parameterizing them into pitch, power, and deviation of power. Based on these parameters, data was classified via decision-tree logic into the emotional elements of anger, joy, sorrow, and calmness. Degree of excitement was also extracted. The system was evaluated by third parties by matching the system performance to human subjective classification for each element. Results indicate that overall matching rate was 70%, and the matching rate was 86% when compared to the subjects' assessment of their own voices. Our system performance exceeded the baseline with non-verbal information, which was equivalent to human subjective assessment.

International Journal of Innovative Computing, Information and Control


Dealing with Acronyms in Biomedical Texts

Recently, there has been a growth in the amount of machine readable information pertaining to the biomedical field. With this growth comes a desire to be able to extract information, answer questions, etc. based on the information in the documents. Many of these desired tasks require sophisticated language processing algorithms, such as part-of-speech tagging, parsing, and semantic interpretation. In order to use these algorithms the text must first be cleansed of acronyms, abbreviations, and misspellings. In this paper we look at identifying, expanding, and disambiguating acronyms in biomedical texts. We present an integrated system that combines previously used methods for dealing with acronyms and Natural Language Processing techniques in new way for a new domain. The result is an integrated system that achieves a high precision and recall. We break the task up into three modular steps: Identification, Expansion, and Disambiguation. During identification, each word is examined to determine if it is an acronym or not. For this,a hybrid approach that is composed of a Naive Bayesian classifier and a set of handcrafted rules is used. We are able to achieve results of 99.96% accuracy with a small training set. During the expansion step, a list of possible meanings for the words determined to be acronyms is created. We break the expansion up into two categories, local and global expansion. For local expansion we use windowing and longest common subsequence to generate the possible expansions. Global expansion requires an acronym database to retrieve the possible expansions.


A CIE Extraction Syatem for CSL Learners

Summary It is obliged to provide an effective reading support system for CSL (Chinese as Second Language) learners, for recognition and comprehension of CIE (Chinese Idiomatic Expression) in Chinese text are very difficult for them. Though most current research are focusing on Chinese reading support, there is still no perfect system that can provide a convenient support aiming at CIE. In this paper, we mainly propose how to help CSL learners recognize and comprehend CIE. We have created a CIE database with 2,305 idiomatic expressions of contemporary Chinese. At the same time we have analyzed the basic structures and using forms of these CIE. By the analysis we have presented an extraction approach which based on rules and characters of these CIE. In our extraction experiment of CCE, the recall achieved 81.65% and the precision achieved 94.34%.

International Journal of Computer Science and Network Security


A New Question Answering System for Chinese Restricted Domain

In this paper, we propose the construction of a web-based Question Answering (QA) system for restricted domain, which combines three resource information databases for the retrieval mechanism, including a Question&Answer database, a special domain documents database and the web resource retrieved by Google search engine. We describe a new retrieval technique of integrating a probabilistic technique based on OkapiBM25 and a semantic analysis which based on the ontology of HowNet knowledge base and a special domain HowNet created for the restricted domain. Furthermore, we provide a method of question expansion by computing word semantic similarity. The system is first developed for a middle-size domain of sightseeing information. The experiments proved the efficiency of our method for restricted domain and it is feasible to transfer to other domains expediently using the proposed method.

本稿では，制限されたドメインの質問回答システムの構築手法を提案する．システムのデータベースには，特別なドメイン文書データベース，Webリソースが含まれる．OkapiBM25に基づいて確率的手法と特別なドメインHowNetに基づいて意味解析を統合する新しい検索手法を提案した．さらに，単語の意味的類似度を計算することによって，問題の拡大方法を開発した．

IEICE Transactions on Information and Systems


A pragmatic emotional model is one of obligatory subjects for building a humanoid computer. This paper presents a conception of a Mental State Transition Network Model using psychological research for reference [1]. The ongoing work proposes a novel approach to detect human emotions. According to Pluchiks basic emotional classification, we defined nine basic emotional states and carried out a series of psychological investigations [2]. By the results of experiments we provide a new way to predict coming humans emotions depending on the various currents emotional states under various reinforcements. By means of statistic data derived from 227 questionnaires, the transitions in distribution among the emotions and relationships between internal mental situations and external stimuli are concluded. The high precision achieved in our evaluating experiments shows the model is helping in recognition and synthesis of human emotion.

ヒューマノイドコンピュータを作るには現実的な感情モデルが欠かない．本文は，参考用の心理学研究を用い，精神状態遷移ネットワークモデルの概念を提出する．続いて人間感情を検出する独創的な方法を提案する．Pluchikの基本感情分類により，我々は九種の基本感情状態を定義し，一連の心理調査を行った．実験の結果を通して，多数の手段で強化された様々な現行の感情状態に基づく，我々は人間の次の感情を予測する新たな方法を得た．227回アンケートからの統計データにより，感情と，内部精神状態と外部刺激の関係，二つの間の分布の遷移が存在しないことが明らかになった．我々の評価実験から得た高精度はこのモデルが人間の感情の認識と合成に役に立てると言える．


Translation Invariant RI-spling Wavelet and Its Application on DE-noising

Wavelet Shrinkage using DWT has been widely used in de-noising although DWT has a translation variance problem. In this study, we solve this problem by using the translation invariant DWT. For this purpose, we propose a new complex wavelet, the Real-Imaginary Spline Wavelet (RI-Spline wavelet). We also propose the Coherent Dual-Tree algorithm for the RI-Spline wavelet and extend it to the 2-Dimensional. Then we apply this translation invariant RI-Spline wavelet for translation invariant de-noising. Experimental results show that our method, when applied to ECG data, the medical image and the textile surface inspection can obtain better de-noising results than that of conventional Wavelet Shrinkage.

International Journal of Information Technology & Decision Making


Effects of Phoneme Type and Frequency on Distributed Speaker Identification and Verification

In the European Telecommunication Standards Institute (ETSI), Distributed Speech Recognition (DSR) front-end, the distortion added due to feature compression on the front end side increases the variance flooring effect, which in turn increases the identification error rate. The penalty incurred in reducing the bit rate is the degradation in speaker recognition performance. In this paper, we present a nontraditional solution for the previously mentioned problem. To reduce the bit rate, a speech signal is segmented at the client, and the most effective phonemes (determined according to their type and frequency) for speaker recognition are selected and sent to the server. Speaker recognition occurs at the server. Applying this approach to YOHO corpus, we achieved an identification error rate (ER) of 0.05% using an average segment of 20.4% for a testing utterance in a speaker identification task. We also achieved an equal error rate (EER) of 0.42% using an average segment of 15.1% for a testing utterance in a speaker verification task.

本論文では，分散型話者識別と検証におけるいくつかの問題を解決するため非伝統的な解決手法を提案した．ビットレートを低減するために，音声信号をクライアントにセグメント化され，話者認識のために(それらの種類および頻度に応じて決定される)最も効果的な音素を選択し，サーバに送信される．話者認識は，サーバーで行われた．ヨホコーパスにこのアプローチを適用することは，話者識別タスクでテスト発話のための20.4%の平均セグメントを使用して，0.05%の識別誤り率(ER)を達成した．


Translation of Japanese Noun Compounds at Super-Function Based MT System

The translation of noun compounds has become a major issue in Machine Translation (MT) due to their frequency of occurrence and high productivity. In our previous studies on Super-Function Based Machine Translation (SFBMT), we have found that noun compounds are very frequently used and difficult to be translated correctly, the overgeneration of noun compounds can be dangerous as it may introduce ambiguity in the translation. In this paper, we discuss the challenges in handling Japanese noun compounds in an SFBMT system, we present a shallow method for translating noun compounds by using a word level translation dictionary and target language monolingual corpus.

本論文では日中機械翻訳における新しい複合名詞の自動翻訳手法を提案する．提案手法は対訳コーパスを必要とせず，目的言語コーパスのみから翻訳ルールを作成できるという特長がある．我々はスーパー関数に基づく機械翻訳システムに同手法を1モジュールとして導入し，評価実験を行い，その有効性を確認した．

IEEJ Transactions on Electronics, Information and Systems


A Preliminary Research of Chinese Emotion Classification Model

There have been some studies about spoken natural language dialog, and most of them have successfully been developed within the specified domains. However, current human-computer interfaces only get the data to process their programs. Aiming at developing an affective dialog system, we have been exploring how to incorporate emotional aspects of dialog into existing dialog processing techniques. As a preliminary step toward this goal, we work on making a Chinese emotion classification model which is used to recognize the main affective attribute from a sentence or a text. Finally we have done experiments to evaluate our model.

自然言語の話し言葉の会話に関する研究はいくつある，その大部分は特定の領域での開発が成功した．だが，現在の人間とコンピュータのインターフェースはただそれらのデータを取って処理することに過ぎない．感情的な会話システムを目指し，我々は会話の感性的な方面を現存の会話処理技術に組み込む方法を試す．この目標を達すための先行として，我々は，一文や一つのテキストの主な感情属性を認識でき，中国語における感情分類モデルの作成に取り組む．最後に，いくつの実験を通してこのモデルを評価する．


The Recognition system of CCE for CSL Learners

CSL (Chinese as Second Language) learners need relevant reading support because recognition of CCE (Chinese Conventional Expression) is a difficulty for them. In this paper, we mainly propose how to help CSL learners recognize CCE in Chinese text. We have created a CCE data-base with 2,305 conventional expressions of contemporary Chinese. At the same time we have analyzed the basic structures and application forms of these 2,305 conventional expressions. By the analysis we have presented an extraction approach which based on rules and characters of these CCE. In our extraction experiment of CCE, the recall achieved 81.65% and the precision achieved 94.34%.

CCE (Chinese Conventional Expression,中国語の固有表現)を認識するのは困難であるため，CSL (Chinese as Second Language,中国語を第二言語とした)学習者には，それに関連する読書支援が必要になっている．本文では，我々は主に，中国語テキストのCCEの認識について，どの様に学習者の支援になるかを提案する．我々は2305個現代中国語の固有表現を含むCCEデータベースを構築し，同時にこれらの固有表現の基本構造とアプリケーションフォームを分析した．分析の結果により，我々はそれらのCCEのルールと特徴値に基づく抽出方法を提供する．抽出実験では，81.65%のリコール率と94.34%の精度を得た．


Stemming to Improve Translation Lexicon Creation form Bitexts

Arabic is a morphologically rich language that presents significant challenges to many natural language processing applications because a word often conveys complex meanings decomposable into several morphemes (i.e. prefix, stem, suffix). By segmenting words into morphemes, we could improve the performance of English/Arabic translation pair's extraction from parallel texts. This paper describes two algorithms and their combination to automatically extract an English/Arabic bilingual dictionary from parallel texts that exist in the Internet archive after using an Arabic light stemmer as a preprocessing step. Before using the Arabic light stemmer, the total system precision and recall were 88.6% and 81.5% respectively, then the system precision an recall increased to 91.6% and 82.6% respectively after applying the Arabic light stemmer on the Arabic documents. The algorithms have certain variables which values can be changed to control the system precision and recall. Like most of the systems do, the accuracy of our system is directly proportional to the number of sentence pairs used. However our system is able to extract translation pairs from a very small parallel corpus. This new system can extract translations from only two sentences in one language and two sentences in the other language if the requirements of the system accomplished. Moreover, this system is able to extract word pairs that are translation of each others, synonyms and the explanation of the word in the other language as well. By controlling the system variables, we could achieve 100% precision for the output bilingual dictionary with a small recall.

本論文では，並列テキストから英語/アラビア語の変換対の抽出の性能を向上させる手法を提案した．いろいろな実験により，提案手法の有効性を確かめることができた．


Nonparametric Speaker Recognition Method using Earth Mover's Distance

In this paper, we propose a distributed speaker recognition method using a nonparametric speaker model and Earth Mover's Distance (EMD). In distributed speaker recognition, the quantized feature vectors are sent to a server. The Gaussian mixture model (GMM), the traditional method used for speaker recognition, is trained using the maximum likelihood approach. However, it is difficult to fit continuous density functions to quantized data. To overcome this problem, the proposed method represents each speaker model with a speaker-dependent VQ code histogram designed by registered feature vectors and directly calculates the distance between the histograms of speaker models and testing quantized feature vectors. To measure the distance between each speaker model and testing data, we use EMD which can calculate the distance between histograms with different bins. We conducted text-independent speaker identification experiments using the proposed method. Compared to results using the traditional GMM, the proposed method yielded relative error reductions of 32% for quantized data.

IEICE Transactions on Information and Systems


Acoustic Model Adaptation for Codec Speech based on Learning-by-Doing Concept

Recently, personal digital assistants like cellular phones are shifting to IP terminals. The encoding-decoding process utilized for transmitting over IP networks deteriorates the quality of speech data. This deterioration causes degradation in speech recognition performance. Acoustic model adaptations can improve recognition performance. However,the conventional adaptation methods usually require a large amount of adaptation data. In this paper, we propse a novel acoustic model adaptation technique that generates speaker-independent HMM for the target environment based on the learning-by-doing concept. The proposedmethod uses HMM-based speech synthesis to generate adaptation datafrom the acoustic model of HMM-based speech recognizer, and consequently does not require any speech data for adaptation. By using thegenerated data after coding, the acoustic model is adapted to codecspeech. Experimental results on G.723.1 codec speech recognition showthat the proposed method improves speech recognition performance. Arelative word error rate reduction of approximately 12% was observed.

Advances in Natural Language Processing Research in Computing Science


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．


Phoneme Based Speaker Modeling to Improve Speaker Recognition System

The most common approach to speaker recognition today is the use of global Gaussian Mixture Models (GMM) which ignores knowledge of the underlying phonetic content of the speech, so it does not take advantage of all available information. In this paper we investigate the phoneme effect on speaker recognition system. We found that some phonemes have strong effect on speaker identification. By segmenting the most effective phonemes for speaker recognition task from a speaker utterance, we could decrease the system complexity and the recognition time. Moreover, this technique is very useful to speed up the authentication process through wire/wireless communication systems. This paper is concerned with improving the performance of speaker recognition systems in two areas: decreasing the identification error rate and decreasing the utterance part required for identification task. We have applied several approaches on YOHO corpus; most of these approaches outperformed previously published results on the speaker identification task. Two of our approaches could achieve 0.0% error rate and one of these two approaches used only an average segment of 13% of the testing utterance for recognition.


Probabilistic Neural Network Based English-Arabic Sentence Alignment

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on a probabilistic neural network (P-NNT) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually aligned training data was used to train the probabilistic neural network. Another set of data was used for testing. Using the probabilistic neural network approach, an error reduction of 27% was achieved over the length based approach when applied on English-Arabic parallel documents.


A Question Answering System on Special Domain and the Implementation of Speech Interface

In this paper, we propose a construction of Question Answering(QA) system, which synthesizes the answers retrieval from the frequent asked questions database and documents database, based on special domain about sightseeing information. A speech interface for the special domain was implemented along with the text interface, using an acoustic model HMM, a pronunciation lexicon, and a language model FSN on the basis of the feature of Chinese sentence patterns. We consider the synthetic model based on statistic VSM and shallow language analysis for sightseeing information. Experimental results showed high accuracy can be achieved for the special domain and the speech interface is available for frequently asked questions about sightseeing information.


Speaker Recognition for Wire/Wireless Communication Systems

Recently data communication spread to the mobile wireless world. The complexity of medium and large speech & speaker recognition systems are beyond the memory and computational resources of the small portable devices. Moreover the most common approach to speaker recognition today is the use of global Gaussian mixture models (GMM) which ignores knowledge of the underlying phonetic content of the speech, so it does not take advantage of all available information. In this paper we address the solution of these two problems by investigating the phoneme effect on speaker recognition system. We used YOHO database for speaker identification task. We found that some phonemes have strong effect on speaker identification. Segmenting the most effective phoneme for speaker recognition task from a speaker utterance and send this phoneme only through the wireless communication system will decrease the complexity of medium and speed up the authentication process though mobile communication system. We have applied different approaches on YOHO corpus, several of these approaches were able outperform previously published results on the speaker ID task. One of our approaches could achieve 0.7% error rate by using only an average segment of 4.45% of the testing utterance for recognition.

The International Arab Journal of Infromation Technology


Packet Loss Concealment Algorithm using Speech Recognition and Synthesis

音声認識·音声合成を用いた音声途切れ補間手法

近年, VoIP技術の発展により, 急速にIP電話が普及しつつある．IP電話の本質的な問題として(特に無線区間がある場合), パケット損失による音声品質の劣化が挙げられる．この問題を解決するため, 音声認識技術と音声合成技術を用いた音声途切れ補間手法を提案する．提案手法は, (1) Missing Feature Theoryに基づく音声認識により, 途切れ区間の音素片列を前後の言語情報及び音響情報から推定し, (2) 推定した音素片列に基づきHMM音声合成を行い, 途切れ区間の音声波形を生成し補間する．本手法で補間した特定女性話者の音声を用いて主観評価実験と客観評価実験を行い, 提案手法の有効性を示す．

The Journal of the Acoustical Society of Japan


Estimating the Minimum Entropy of Chinese and Japanese Languages

The study of minimum entropy of a natural language has been an interesting research subject. For English a great progress has been made, but a few reports on other languages have been found in literature. Based on two hypotheses on conservation of information quantity, we proposed a method, which can be used to estimate the minimum entropy of characters in natural languages. With a large quantity of translation corpus, this method enables us to estimate the minimum entropy without calculating the probability. Besides, as the scale of translation corpus increases, the fluctuation of the ratio between character quantities in any two languages becomes negligible. In this paper, we apply this method to the study of two languages of a large character total, Japanese and Chinese.

自然言語の最小エントロピーの研究は興味深い研究対象となっている．英語の場合は大きな進歩がなされているが，他の言語には，いくつかの報告が文献に発見されている．情報量の保全の2つの仮説に基づいて，我々は，自然言語における文字の最小エントロピーを推定することができる方法を提案した．

International Journal of Information Technology & Decision Making