An Emotion Similarity Calculation Using Dialog Sentence Corpora

In order to estimate an utterance persons emotion from asentence, a dictionary which assigned a emotion attribute for every wordis built, and, only as for which, there are many methods on based on sentencein which there is the word or not. However, there is a case wherethe emotion expressed by the word used simultaneously and word orderdiffers, plentifully. In this paper, in order to solve the above-mentionedproblem, we propose a method for an emotion similarity calculation thathow similar a dialog sentence is to which sentence betoken which emotions.This method defines a formula which calculates the similarity ofan input sentence and the dialog sentence classified according to emotoinbased on BLEU. The experiment calculates the rate which the emotionsare betokened by two dialog sentences became the same. These sentencesare the dialog sentence and the other dialog sentence which most similarto another. We call this rate emotion estimation success rate. The result,the emotion estimation success rate was improved more than onlyusing BLEU.


A Study on Automatic Emotion Tagging in Text Corpus

Proceedings of International Symposium on Artificial Intelligence and Affective Computing 2006


Speech Recognition for the Kanji Last Name Candidates Using Clarifying Information


Picture Processing in Expression Recognition

In recent years, along with the development of information technologyand artificial intelligence technology, the computers` human nature design hasgradually become possibly. In order to provide a better man-machine interactiveenvironment, we hoped the computer can understand and simulate humanity'sperceptual information. Being an important method for human to express thesentimental, the expression is a kind of direct-viewing and bright source material.Therefore, we hoped to establish a system, which enables the computer to recordand distinguishes humanity's expression, through some input devices like cameraand so on, thus to section peoples sentiment condition. In this article, we will dosome introduction and summary about the research work which has been carriedon, and then proposed some new research methods.


Predicting the Emotion from News Articles based on Emotionally Laden Words and Classification of News Articles by Different Opinions

News about the same event can have radically different opinionsand emotions depending on the author, news paper company, andnationality. We assume that these opinions and emotions are related toeach other. In this paper we propose an approach to predict the emotionof news articles based on emotionally laden words, and to classify thenews articles using predicted emotions. The result of having attemptedto predict the emotions in the articles of baseball games and politicalevents, although the approach was effective in the articles of baseballgames, it was not effective in the articles of political events.


An Emotion Estimation Method for Emotion Interface

The existing approaches of emotion estimation from a textwere mainly focused on estimating positive or negative emotions in thetext. However human emotions are more complex and we cannot judgeour emotions only by positive or negative. In this paper, we proposedan emotion estimation algorithm for conversation sentence. We gave therules of emotion occurrence to 1616 sentence patterns. In addition, we developeda dictionary which consisted of emotional words and emotionalidioms. The proposed method can estimate emotions in a sentence bymatching the sentence pattern of emotion occurrence and the rule. Furthermore,we can get two or more emotions included in the sentence bycalculating emotion parameter. We constructed the experiment systembased on the proposed method for evaluation. We analyzed weblog dataincluding 1645 sentences by the system, and conducted the experimentto evaluate emotion estimation accuracy.


Chinese Semantic Dependency Analysis and its Applications

Semantic analysis of natural language is a difficult but extremelydesirable task, because of its many uses. In this paper, an overviewof semantic dependency analysis and its applications are given. A decisiontree based semantic dependency analyzer for Chinese, which we callSEEN, is introduced. Then, an application of semantic dependency inAffective Computing is given. The application determines the emotionsof the actors in Chinese sentences.


Evaluation of Super-Function based Japanese-English Machine translation


A Study on Automatic Text Summarization and News Collection

With the high speed growth of World Wide Web and on-lineinformation services, more and more information is available and accessible online.Meanwhile using mobile devices such as mobile telephone, PDA tobrowse through on-line information has become very popularly. But a largeamount of information and lengthiness information cant fit for the mobiledevices. In order to save this problem, we propose a method which collectsoriginal news text from on-line information and extracts summary from themautomatically. The system is made up by Automatic News Collection and AutoText Summarization. Our experimental results proved the effectiveness of themeans.


Different Approaches for Bilingual Sentence Alignment

In this paper, we present new approaches to align sentences in bilingualparallel corpora based on the use of the linguistic information of the textpair in three different classifiers (feed forward neural network (FF-NNT), probabilisticneural network (P-NNT) and Gaussian mixture model (GMM)). A featureparameter vector is extracted from the text pair under consideration. Thisvector contains text features such as length, punctuate score, and cognate scorevalues. A set of manually prepared training data has been assigned to train oneof these classifiers. Another set of data was used for testing. Using the FF-NNT,P-NNT and GMM classifiers, we could achieve error reduction of 36%, 27%and 50% respectively over length based approach when applied on English-Arabic parallel documents.


A Preliminary Research of Chinese Emotion Classification Model

There have been some studies about spoken natural language dialog, andmost 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 toincorporate emotional aspects of dialog into existing dialog processing techniques.As a preliminary step toward this goal, we work on making a Chinese emotion classificationmodel which is used to recognize the main affective attribute from a sentenceor a text. Finally we have done experiments to evaluate our model.


An Information Retrieval Library for Japanese and English News Articles

Proceedings of International Symposium on Artificial Intelligence and Affective Computing 2006

This paper presents a library of core algorithms for Japanese-English news-based cross-lingual information retrieval. The library isbroken up into three modules: Knowledge Acquisition, Knowledge In-terpretation and Knowledge Translation. This paper presents in detailone algorithm from each module. From the Knowledge Acquisition mod-ule, an algorithm for semi-automatically creating a special domain col-lection is presented. The algorithm is able to create good size documentcollections about a specific topic, person, etc. From the Knowledge In-terpretation module, an algorithm for extracting keywords from a singledocument that does not require a collection or corpus statistics is pre-sented. It is the basis for most of the algorithms in the library. Finally,from the Knowledge Translation module, an algorithm for choosing thebest translation of a text is presented. It uses a combination of statisticalanalysis and similarity measurement to choose the best translation froma set of candidates.


Classification of Facemarks Using Statistics of Characters

In this study, we present an approach for the classification of facemarks into some facial expression categories. Facemarks are some of the expressions that are often used in text-based comunication. Facemarks express human facial expression or action and they help us to understand what writers imply. However, there are some problems in the proccessing of the facemarks with computer; facemarks are numerous and users usually make new facemarks. Therefore, we propose to use the characters in facemarks to classify them. Though the facemarks are various, only some of signs and letters are used for the characters that compose the them. Moreover, there is a feature of the expressions of facemarks in the characters. We present an approach for the characters that compose the facemarks and evaluate a simple method for classification of facemarks by the characters.


A speech emphasis method for noise-robust speech recognition by using repetitive phrase

ICCT International Conference on Communication Technology (ICCT-06)

In this paper, we propose a novel noise-robust speech recognition technique using a repetitive phrase such as "Turn on the TV, Turn on the TV" as a speech command to control household appliance and on-vehicle apparatus. In daily conversation, the use of repetitive phrase with many syllable gives a low appearance ratio, and reduces the incorrect acceptance of a command. Moreover, speech emphasis may be expected by using repetitive phrase because each phrase is not influenced by unexpected noise at same time. In this paper, we propose to use repetitive phrase as speech emphasis. We conduct speech recognition experiment using the speech which are degraded by three kinds of noise: a buzzer, a whistle, and a telephone ring. From this experiment, we proposed method can improve the speech recognition accuracy under the condition of the all noises. Especially, the proposed method improves the speech recognition accuracy (from 25% to 86%) under the condition of the whistle noise.


The Construction of The Facial Expression Video Database

In recent years, we pay attention to the facial expressions which can show the intentions of the speakers unconsciously, and we are trying to presuppose the emotions by using those expressions. In order to further the research, we need a great deal of expression data. However there is a little kinds of expression data and available in the research. Moreover those data are always in the picture type but not the video type that we want. Therefore, we built an facial expression video database in which a lot of expression data had been filled. This paper introduces the facial expression video database built by us.


Speech bandwidth extension method using speech recognition and speech synthesis

In this paper, we propose two kinds of novel speech bandwidth extension methods based on a speech recognition, a speech synthesis and a speech signal processing technique. In the proposed methods, the lost speech component by bandlimiting is generated with the technique of a speech recognition, a speech synthesis, and a speech signal processing by using the bandlimited speech information. For evaluating both proposed methods, we conducted the subjective and objective evaluation experiments. The experimental results shows the effectiveness of both proposed methods about the extension feeling of bandwidth. However, the evaluation of the speech quality became a not good result. Our problem is an improvement of the speech quality of the proposed method in the future.


Wind noise reduction method for speech recording using multiple noise templates and observed spectrum fine structure

Wind noise affects the quality of the speech recording in the field. In this paper, we propose a wind noise reduction method based on a signal processing technique. In the method, spectrum envelopes of wind noise are estimated by using referential multiple wind noise templates and the low freqency band spectrum envelope of the observed signal. Then, we estimate the noise spectrum using the estimated spectrum envelope and the fine structure of the observed signal. Finally, we subtract this estimated noise spectrum from the observed signal. For evaluating the proposed method, we conducted subjective and objective evaluation. The experimental results showed that the proposed method achieved much better improvement than conventional spectral subtraction.


A Chinese Automatic Text Summarization system for mobile devices

The Pacific Asia Conference on Language, Information and Computation

A large amount of on-line information and lengthiness information can't fit for the mobile devices. In order to save this problem, we propose a method which collects original news text from on-line information and extracts summary sentences from them automatically. On this basis, we adopt WML(Wireless Markup Language) to build a news website for mobile devices browsing through the news summary. The system is mainly made up by Automatic News Collection and Auto Text Summarization. Our experimental results proved the effectiveness of the means.


In the present study, we present different approaches for transliteration proper noun pair's extraction 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 words. We evaluate the presented new approaches using an English- Arabic parallel corpus. Most of our results outperform previously published results in terms of precision, recall and F- Measure.


Lost Speech Reconstruction Method using Speech Recognition based on Missing Feature Theory and HMM-based Speech Synthesis

International Speech Communication Association

In recent years, IP telephone service has spread rapidly. However, an unavoidable problem of IP telephone service is deterioration of speech due to packet loss, which often occurs on wireless networks. To overcome this problem, we propose a novel lost speech reconstruction method using speech recognition based on Missing Feature Theory and HMM-based speech synthesis. The proposed method uses linguistic information and can deal with the lack of syllable units which conventional methods are unable to handle. We conducted subjective and objective evaluation experiments under speaker independent conditions. These results showed the effectiveness of the proposed method. Although there is a processing delay in the proposed method, we believe that this method will open up new applications for speech recognition and speech synthesis technology.


REQUIRED PRECISION TO NATURAL LANGUAGE PROCESSING FOR THERAPEUTIC PATIENT-HEALTH CARE PROVIDER COMMUNICATION

Proceedings of the 9th IASTED International Conference on Intelligent Systems and Control

The International Association of Science and Technology for Development

It is necessary to approach the caring humans perform by achieving empathetic understanding in human-robot communication and expressing caring feelings for a human-supporting robot. This paper aims at requiring precision to natural language processing for therapeutic patient-health care provider communication. First, our subjects were asked to view a video, which was to induce emotions. And then, an interview was carried out in order to identify the scenes that caused changes in the subjects' emotion. Also, the scenes were objectively judged by measuring the activity of an electroencephalogram (EEG) of the subjects. Finally, the lines of the scenes were extracted and, taking the subjects' impressions of their empathy as a criterion for judgement of natural language processing (NLP), compared with the analysis results by NLP. By our expe! riment, although it was difficult to make a judgment on the impressions in which emotions were intermingled such as joy and sorrow, NLP and the criterion for judgement that was the empathetic understanding of the subjects were consistent. It is clear that an empathetic understanding performed in medical welfare sites should be able to sensitively perceive pain, distress and loneliness of the subjects in particular.


SEMI-AUTOMATIC EMOTION RECOGNITION FROM CHINESE TEXT

The interest in emotion recognition has attracted more and more attentions. There have been some studies about emotion extraction from spoken natural language dialog, and most of them have successfully been developed within the specified domains. But computer application on Chinese Natural Language Processing (NLP) is still on the starting stage. Aiming at developing a Chinese affective dialogue system, we have been exploring how to incorporate emotional aspects of dialogue into existing dialogue processing techniques. However, in Chinese linguistic corpora only few of them include emotional information. As a preliminary step toward this goal, we work on constructing what we call an emotion-thesaurus, as the base of our emotion recognition system. Then we construct the classification system using the thesaurus. Finally we give the evaluation of our system.


A Machine Learning Approach to Determine Semantic Dependency Structure in Chinese

Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference


Determining the Emotion of News Articles

Authors of news stories through their choice in words and phrasing inject an underlying emotion into their stories. A story about the same event or person can have radically different emotions depending on the author, newspaper, and nationality. In this paper we propose a system to judge the emotion of a news article based on emotion word, idiom and modifier dictionaries. This type of system allows one to judge the world opinion on varying topics by looking at the emotion used within news articles about the topic.


Statistical Analysis of a Japanese Emotion Corpus for Natural Language Processing

In this paper, we build a Japanese emotion corpus and perform statistical analysis on it. We manually entered in about 1,200 example dialogue sentences. We collected statistical information from the corpus to analyze the way emotion is expressed in Japanese dialogue. Such statistics should prove useful for dealing with emotion in natural language. We believe the collected statistics accurately describe emotion in Japanese dialogue.


A Hybrid Expert System for Stock Market Analysis - from Neural forecasting to Linguistic Expression

Proceedings of The Fourth International Conference on Information and The Fourth Irish Conference on the Mathematical Foundations of Computer Science and Information Technology


Category Classification and Topic Discovery of News Articles


Automatic extraction of Super-Function from corpus and experimental evaluation


Psychoms: A mental health patient management and variance analysis system 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 purpose of this paper is considering a way to develop the Psychoms that mental health patient management and variance analysis system using artificial intelligence. This system aims to improve a patient's quality of life through consistent and timely care and has potential to assist in the improvement of quality health care services. This article describes a method of outcomes management and variance analysis in this system and the prospective development of future research are discussed.


Consideration of Measuring Method for Empathic Understanding as Technological Competency of Nursing


Chinese Idiomatic Expression Reading Support for Foreign Learners


Maximum Entropy Based Semantic Analysis of Chinese


Affective Information Processing and Recognizing Human Emotion

The Fourth International Conference on Information and The Fourth Irish Conference on the Mathematical Foundations of Computer Science and Information Technology


PHONEME BASED SPEAKER MODELING TO IMPROVE SPEAKER IDENTIFICATION

ICGST International Conference on Artificial Intelligence and Machine Learning

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 were able outperform previously published results on the speaker identification task. One of our approaches could achieve 0.0% error rate using only an average segment of 13% of the testing utterance for recognition.


Study of Intra-Speaker's Speech Variability over Long and Short Time Periods for Speech Recognition

Proc. of 2006 IEEE International Conference on Acoustic, Speech, and Signal Processing (ICASSP2006)

In this paper, we describe a Japanese speech corpus collected for investigating the speech variability of a specific speaker over short and long time periods and then report the variability of speech recognition performance over short and long time periods. Although speakers use a speaker-dependent speech recognition system, it is known that speech recognition performance varies pending when the utterance was uttered. This is because speech quality varies by occasion even if the speaker and utterance remain constant. However, the relationships between intra-speaker speech variability and speech recognition performance are not clear. Hence, we have been collecting speech data to investigate these relationships since November 2002. In this paper, we introduce our speech corpus and report speech recognition experiments using our corpus. Experimental results show that the variability of recognition performance over different days is larger than variability of recognition performance within a day.


A Machine Learning Approach to Determine Semantic Dependency Structure in Chinese

Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference

In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and semantic dependency relations. An initial investigation is then done using a Naive Bayesian Classifier and some handcrafted rules. The results show that the algorithms and proposed approach are effective at determining semantic dependency structure automatically. The Naive Bayesian Classifier makes a good baseline algorithm for future research.


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

Proceedings of Nonlinear Circuits and Signal Processing (NCSP'06)

In this paper, we propose a wind noise reduction method based on a signal processing technique. In this method, spectrum envelopes of wind noise are estimated by using referential multiple wind noise templates. Then, we estimate the noise spectrum using the estimated spectrum envelope and the fine structure of a observed signal. Finally, we subtract this estimated noise spectrum from the observed signal. For evaluating the proposed method, we conducted subjective and objective evaluation. The experimental results showed that the proposed method achieved much better improvement than conventional spectral subtraction.


Natural Language Processing Workshop-Beijing University of Posts and Telecommunications


Data Collection for Investigating Speech Variability in a Specific Speaker Over Long and Short Time Periods

Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE'05)

In this paper, we describe a Japanese speech corpus collected for investigating the speech variability of a specific speaker over short and long time periods. Although speakers use a speaker-dependent speech recognition system, it is known that speech recognition performance varies pending when the utterance was uttered. This is because speech varies even if the speaker utters a specific sentence. However, the relationship between intra-speaker speech variability and speech recognition performance is not clear. We have not seen a corpus of Japanese speech data of a specific speaker over a long time period. Hence, since 2002, we have been collecting speech data for investigating the relationships between speech variability and speech recognition performance. In this paper, we introduce our speech corpus and conduct speech recognition experiments. Experimental results show that the variability of recognition performance over different days is larger than variability of recognition performance within a day.


An Emotion Information Processing Model based on a Mental State Transition Network

A machine that lacks of emotion computing ability cannot realize artificial intelligent sufficiently and cannot meet the increasing demanding of human-computer interaction as well. Though most current research is focusing on physical components of emotions, rarely are they carried out from the view of psychology. In this paper an Emotion Information Processing Model based on the Mental State Transition Network and a corpus of common sense [1] are proposed to detect human emotion. By a series of psychological experiments, we present a new way to predict future human's emotions depending on the various current emotional states under various conditions. Besides, people in different sexes characters and ages are taken into consideration in our experiments. From the psychological experiments data that is abstracted from 250 questionnaires, a Bayesian network for describing the transitions in distribution among the emotions and relationships between internal mental situations and external reinforcements are concluded. Further more comparing seven relative evaluating experiments we found that the model provided a higher precision average rate of 0.843 respectively for the 50 random data examples.


Web-based Question Answering System for Restricted Domain Based of Integrating Method Using Semantic Information

In this paper, we propose the construction of a webbased Question Answering(QA) system for restricted domain that combined three resource information databases for the retrieval mechanism, including a Question &Answer database, a special domain documents database and a 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 based on an 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-sized 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.


Parallel corpora have become an essential resource for work in multi lingual natural language processing. However, sentence aligned parallel corpora are more efficient than nonaligned parallel corpora for cross language information retrieval and machine translation applications. In this paper, we present a new approach to aligning sentences in bilingual parallel corpora based on the text character length between successive punctuates. 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 punctuates matching decreased and the sentence alignment precision increased. Using this new approach, we could achieve 21.8% improvement over length based approach when applied on English-Arabic parallel documents.


Chinese Conventional Expression Reading Support System for Japanese

Understanding of Chinese conventional expression is a big difficulty for the Japanese readers who want to know the natures of Chinese language and culture properly. Though most current researches are focusing on Chinese reading support, there is still no perfect system that can provide a convenient supporting aiming at Chinese conventional expressions. In this paper a practical Chinese Conventional Expression Reading Support System is presented from the viewpoint of recognition science. And we have also created a Chinese Conventional Expression Database with 2305 conventional expressions of contemporary Chinese. At the same time we have analyzed the basic structures of these 2305 conventional expressions. By the analysis of the database we have classified three primary categories of these conventional expressions. Moreover, we have used the approach based on rules to recognize the Chinese conventional expressions.


Estimating Emotion Changes Using Electroencephalographic Activities and its Clinical Application

In the care, the communication based empathetic understanding is important. We have examined how to grasp empathetic understanding. 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.


Semi-automatic Emotion Recognition from Textual Input Based on the Constructed Emotion Thesaurus

After parsing an important task is to determine the semantic structure of sentences. In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and dependency relations. Two supervised machine learning algorithms with varying features were then used to learn the relations. Finally, a set of rules were created based on features of Chinese to solve some problem patterns that were found in the Penn Chinese Treebank dealing with ambiguous structures. The experimental results show that the algorithms and proposed approach are effective for determining semantic dependency structure automatically.


Multilingual Single Document Keyword Extraction for Information Retrieval


Automatically Determining Semantic Relations in Chinese Sentences


A lost speech reconstruction method using linguistic information

In recent years, IP telephone use has spread rapidly thanks to the development of VoIP (Voice over IP) technology. However, an unavoidable problem of the IP telephone is deterioration of speech due to packet loss, which often occurs on the wireless network. To overcome this problem, we propose a novel lost speech reconstruction method using speech recognition and synthesis. This proposed method uses linguistic information and can deal with the lack of syllable units which conventional methods are unable to handle. We conducted subjective and objective evaluation experiments. These results showed the effectiveness of the proposed method. Although there is a processing delay in the proposed method, we believe that this method will open up new applications for speech recognition and speech synthesis technology.


Frequency Characteristic Normalization Method using Blind Equalization Technique with Multiple References for DSR

Proceedings of 10th International Conference SPEECH and COMPUTER (SPECOM2005)

In this paper, we propose a blind equalization method with multiple references for ETSI standard Distributed Speech Recognition (DSR) front-end. The proposed method compensates for acoustic mismatch caused by input devices. In ETSI advanced DSR front-end, the blind equalization method is introduced to compensate for acoustic mismatch. This method estimates the bias, which compensates for the mismatch, using one reference vector. If the input speech is short or contains many similar phonemes, there is concern that this method might not estimate the accurate bias. The proposed method estimates the bias using multiple references instead of one reference. Using multiple references, the proposed method estimates the bias more accurately. In addition, we represent the references by combining the VQ centroids used in the data compression process of ETSI standard DSR front-end. This limits increases in memory size and computation costs on the front-end. As a result, we can carry out this method in real-time on the DSR front-end. Experimental results on a Japanese newspaper dictation task indicate that the proposed method gave better performance under acoustic mismatched conditions than the conventional blind equalization method.


Intelligent Tutoring Systems: Research Status and Its Development in China

Proceedings of IEEE International Conference on Natural Language Processing and Knowledge Engineering

The first application of artificial intelligence (AI) to education has been to build intelligent tutoring systems (ITS). With the development of modern information technologies, ITS has been increasingly applied in education more and more widely. The applications of ITS in education have been changing the traditional instructional model and making learning more effective and meaningful. In this paper, the definition, architecture, and characteristics of ITS, some typical ITS and the current status, as well as its research focus of ITS are presented. Then the current research and applications of ITS in China are introduced. In the end, the paper discusses the future development of ITS.


Distributed Speaker Recognition using Speaker-Dependent VQ Codebook and Earth Mover's Distance

In this paper, we propose a distributed speaker recognition method using a non-parametric 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 speakerdependent 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 textindependent speaker identification experiments using the proposed method. Compared to results using the traditional GMM, the proposed method yielded relative error reductions of 85% for quantized data.


Improved Semantic Similarity Computation in Question-Answering System

Proceedings of the Ninth IASTED International Conference on Artificial Intelligence and Soft Computing

In this paper we propose an improved semantic similarity computation method for a Chinese question answering (QA) system [1]. The question answering system is capable to find out what answer are satisfy users requirements. Most successful QA systems aim at English. The conventional method used in Chinese QA systems for retrieving the related answers is Vector Space Model (VSM) in natural language processing (NLP). The VSM can only search by the keywords in the questions without the semantic relative information. The searching method in terns of semantic similarity considers not only the keywords but also the words related with keywords in semantics. We employ an improved method to use How net [2], a lexical knowledge base to calculate the word similarity and then the sentences similarity. The methods have been applied in our Japan Travel Question Answering System and the experiments are presented with satisfying results.


Facial Feature based Expression Recognition for an Affective Interface

We focus on human psychological characteristics to de velop general-purpose agents that can recognize human emotion and create machine emotion. In this paper a sys tem for identifying facial expressions by using facial fea tures is presented. The system recognizes 7 facial expres sions. The 7 facial expressions are made up of 6 basic emotional expressions (happiness, sadness, surprise, fear, anger, and dislike) and one non-expression. The Facial Ac tion Coding System (FACS) is used to make the resulting system robust. For identication, the shortest distance be tween the input features and features stored in a dictionary is used. From these facial expressions a users intention can be extrapolated and used to improve the human computer interaction experience.


Language-independent Computer Emotion Recognition


Research on development of the Hospital Management Outcomes Analysis System for using in psychiatric hospitals

Recently, clinical pathways (CP) are often implemented to improve care, yet their effect on quality of care and outcomes have not been generally evaluated. Especially, the item of CP and the outcome management for the patient with mental disorders is not examined enough in Japan. The purpose of this paper is to examine the development of prototype Hospital Outcomes Analysis System (HOAS). As the first step of HOAS development, authors considered to create CP and outcome management item for people suffering schizophrenia for the improvement of quality mental health care. Comprehensive survey interviews were conducted for the hospital staff. CP for the patient with schizophrenic and the critical criteria for patient outcome management (OMCP) were developed from these surveys. Additionally, in order to fully utilize CP, it is very important to develop the efficient and effective means for managing patient outcomes. The CP and outcome management in the hospital may bring patients and their families in progress QOL, in addition to job satisfaction of care-givers. As the second step, we developed a prototype HOAS based on OMCP during the whole period from hospitalization to discharge of which patient outcomes management is done.


Analysis of electroencephalographic activity in condition of emotional activation in human

In the care, the communication based empathetic understanding is important. We have examined how to grasp empathetic understanding. As the first step, we study the characteristics of the electroencephalographic activity during subjects impressed time. We recorded electroencephalogram (EEG) of thirteen healthy female aged 19-29 years when the subjects were watching video. There were increases of the amplitudes of EEG during impressive scene of video. According to observation replayed EEG on display, as a result of analyzing two subjects' data among 13 persons, increase of amplitude was found firstly in Fp1, Fp2. Afterwards, increase of amplitude was gradually observed in F, O and T area. When healthy young females become impressed, the amplitude of their EEG increases. The more they are impressed, the more amplitude of their EEG increases.


Mental State Transtion Network and Psychological Experiments

Proceedings of the Ninth IASTED International Conference on Artifical Intelligence and Soft Computing

Information recognition and extraction of human emotions from words and actions, facial expression and sensibil ity are necessary for machines in order to communicate smoothly with humans and to realize affective communica tion. We focus on human psychological characteristics to develop general-purpose agents that can recognize human emotions and create machine emotions. We comprehen sively analyze voice sounds and picture images that rep resent information included in emotion elements such as phonation, facial expressions, and language expressions. We analyze and estimate extensive statistical data based on the latest achievements of brain science and psychology in order to derive transition networks for human psycho logical states. We establish a speaker word model for the computer to simulate its own psychological transitions and emotional presentations, which is aimed to develop affec tive interface and establish theoretic structure and realiza tion method for emotion communication. In this paper, a Mental State Transition Network is presented and an affec tive interface based on the presented network is proposed, and some psychological experiments are given.


An Experimentation on Creating a Mental State Transition Network

IEEE International Conference on Information Acquisition

Studies on the model of artificial psychology are 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 is presented and a psychological experiment based on the model is carried out. In this work, we hypothesize that internal mental movements of a human depend on a Mental State Transition Network that is made up of seven discrete emotional states. And we propose a new method to predict the emotional transitional probability distribution by using psychological questionnaires. The transition probability distribution of the model is calculated through a psychological experiment and the model provided a relatively high precision rate of 0.872 for the 50 random data examples used in a experimentation.


Estimating Human Emotions using Words and Sentence Patterns

Our research group proposes a method to recognize and create human emotions which can be applied to a robot used for welfare services. We also aim to develop an affective computer with enough ability to communicate that prople don't sense a disconnect. In this paper, an "emotion estimation module" based on words for recognizing human emotions. This mudule extracts "emotion occurrence condition" based on emotions contained in words and sentence meanings, and estimates the emotion of a speaker or writer. The meterials used for the module are collected from written quotations from conversations and internet blogs. We constructed a prototype system based on the proposed method and conducted an experiment on sentences taken from internet blogs in order to verify the effectivenss of the method.


Speaker Recognition using a Non-parametric Speaker Model Representation and Earth Mover's Distance

Proc. of International Workshop on statistical modeling approach for speech recognition, "BEYOND HMM"

In this paper, we propose a distributed speaker recognition method using a non-parametric 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.


An Interative e-Learning System Using Natural Language Processing Technology

Proceedings of the third International Conference on Information

In recent years, the remote education system using television and internet has been developed and applied not only in open universities and preparatory schools, but also in areas where educational resources are scarce. However, this system can not make response to the questions from students in real time. Therefore, we introduce a new interactive E-Learning system using natural language processing technology through which questions can be received and answered in real time.


Robust Distributed Speaker Recognition to Solve the Differences in Frequency Characteristic Problem

Robust distributed speaker recognition to solve the differences in frequency characteristic problem Abstract:Recently data communication needs spread among wireless devices. However portable, wireless devices lack the memory and computational resources to handle medium and large, complex speech and speaker recognition systems. To overcome this problem, ETSI standard DSR front-end, running at client nodes, extracts the speech parameters, compresses them using split vector quantization and sends them to a remote server DSR back-end. However the VQ step for feature compression distorts the transmitted data which increases the speaker recognition error rate. In this paper, we conducted experiments for text independent speaker identification on a Japanese speech corpus. To decrease the effect of vector quantization distortion, we used the Bias Removal Method (BRM). BRM decreased the identification error rate by 16%.


A Junior High School Science Education Support System Using Natural Language Processing Techniques

In this paper, a new method for constructing a junior high school science education support system using natural language processing technique is presented, and the outline of an experimental system based on the proposed method is described. In the system, the input sentence is analyzed and the science question sentences are generated automatically by using Super Function. The system can give more interesting questions and answers providing students pleasant learning environment. Moreover, the speech recognition and the voice synthesis are used for the system interface.


Speaker Recognition for Mobile 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 some 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.


Chinese Korean Machine Translation System Using Super-Function

This paper describes how Super-Function Based Machine Translation (SFBMT) is used to translate original language (Chinese) sentences into the target language (Korean) sentences. SFBMT uses Super Function (SF) to translate without thorough syntactic and semantic analysis as most Machine Translation (MT) systems usually do. The SF is a function that shows the correspondence between original language sentence patterns and target language sentence patterns. Here, the proposed SFBMT Chinese-to-Korean is efficient in aiding human to browse documents written in Korean with Chinese indication.


Natural Language Generation for Technical Analysis of Stock Market Using Extended Super-Function

In the last few years, neural network and other advanced methods have been used for the technical analysis of stock market. However, these methods have been very difficultly understood by non-expert users to improve their multivariate prediction ability for stock market. In this note, this paper discusses the usefulness of neural network model and other advanced methods for the technical analysis of stock market. The analytic results need to be expressed automatically in natural language for non-expert users. So this paper describes a natural language generation system based on extended super-function to express analysis information of stock market. This system is designed to collect a corpus of translation pairs between mathematic expression and language expression of technical analysis, then uses the extended super-function method to construct translation algorithm including comprehensive grammars and to generate the closest language expression.


An Algorithm for Measuring Human Emotions based on Context and Sentence Pattern

It is necessary to nurse the senior citizen for declining population of children and increasing nuclear family. We are researching and developing a welfare robot which has a sensibility to support the life of the senior citizen and to reduce nursing. One of the basic researches on such welfare robots is to measure the sensibility from conversation sentences. In this paper, an algorithm for measuring the sensibility is presented, and a prototype system based on the proposed algorithm has been constructed.


Super-Function based Japanese English Machine Translation Experiment and Evaluation

We present a new method called Super Function Based Machine Translation(SFBMT). SFBMT uses Super Function(SF) to translate without syntactic and semantic analysis as most conventional MT systems do. Using a SF, a translated sentence is fluent and translation speed is very fast. However, (1)The generality as a function is lost, because SF becomes longer as a sentence becomes long, (2)Specification of a candidate cannot be performed if SF has two or more candidates. In this paper, we experimented for the sentence which has extracted SF in order to investigate the cause of these two problems. As the experimental results, we proved that long SF can be divided into some short SF. Besides we proved that an English preposition and an English verb are changing with the attributes of a noun.


A Super-Function Based Japanese Chinese Machine Translation System

Proceedings of the third International Conference on Information

Machine Translation (MT) tools are based on advanced computational linguistic analysis. MT ''engines'' linguistically process source documents to create a translation ''from scratch.'' In this paper, a Super-Function (SF) based Japanese-Chinese MT system is presented. The system uses the SF approach to do the translation without going through syntactic and semantic analysis as many MT systems usually do. The SF is a function that shows the correspondence between original language sentence patterns and target language sentence patterns. In this paper, we aim at performing MT between Japanese and Chinese to translate business letters by the SF based technique.


A method for processing Chinese ``BA'' sentence in Chinese Japanese machine translation system

There is a common but difficult problem in a Chinese-Japanese machine translation system, which is to process ``BA'' sentence. ``BA'' sentence is a sentence that has a prepositional word of ``BA''. Structural character of ``BA'' sentence is that the original verb is put behind the object word. The object word after preposition ``BA'' is used as an adverbial modifier of an active word. Nowadays, a practical Japanese-Chinese machine translation system based on translation rules has been developed. However, the current system is still unable to solve the problem when a complex ``BA'' sentence appears. This paper presents a method for processing Chinese ``BA'' sentence in Chinese-Japanese machine translation system. First of all, a large number of grammar items form Chinese grammar books are collected, some elementary judgment rules are set by classifying and inducing 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. At the end, a five-segment model used for ``BA'' sentence translation is brought forward after above mentioned analysis.


Chinese Automatic Question Answwering System Based on Natural Language Feature and VSM

In this paper, we propose a question-answering construction, which synthesizes the answers retrieved to the questions most frequently asked, based on common knowledge and text knowledge about sightseeing information. In order to improve replies more accurately, one must consider the synthetic model based on statistic VSM and the shallow semantic analysis, so that the domain is only limited to sightseeing information. Lastly, the result is obtained by evaluation experiments, where high accuracy can be achieved when the results of retrieval are seen as correct, if the correct answer has appeared up to the third higher rank which sorts the value of the resembling degree. The experiments proved the efficiency in our method and it is feasible to use this method to develop Question-answering technology.


Robust Recognition of Expression for Individual and Generality

To recognize face expression is very important issue in natural language understanding. In this paper, a new method for recognizing face expression, which consists of a preprocessing face expression image process, a detecting face area process, a extracting the characteristic process, and a recognizing the expression process, is presented. The recognized expressions in our research are seven kinds of expressionless, happy, surprise, disgust, fear, sadness and anger. In the method, for improving the system robustness, the face feature for each face expression is extracted by using FACS, and then they are compared with a face expression dictionary using a minimum distance method.


Histogram-based Speaker Recognition Method for Distributed Environment

In this paper, we focus on a distributed speaker recognition technique, in which quantized feature vectors are sent to a server, as with distributed speech recognition. 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, we propose a novel non-parametric speaker recognition method based on histogram comparison. The proposed method calculates the distance between a histogram of registered quantized feature vectors and a histogram of testing quantized feature vectors. To measure distance, we use Earth Mover's Distance (EMD). The EMD has recently been successfully applied to image retrieval. We conducted text-independent speaker identification experiments using the proposed method. When compared to results using the traditional GMM, the proposed method yielded relative error reductions of 80% for quantized data.


Evaluation of frequency characteristic normalization method with multiple reference cepstrum on the Japanese newspaper article sentences speech corpus

本論文では，分散型音声認識システムにおける入力系の周波数特性の違いによる音声認識精度の低下を抑制するため，複数の参照ケプストラムを用いた周波数特性正規化手法を提案した．提案手法は，フレーム同期で駆動し，各フレームで最近の参照ケプストラムを探し，周波数特性を正規化するバイアスを計算する手法である．提案手法の有効性を検証するため日本語連続音声認識実験を行った結果，提案手法は入力系の周波数特性が変動しても，音声認識精度の低下は少なく，有効であることがわかった．


Image Processing Using 2-D Translation Invariant RI-Spline Wavelet

IEEE International Conference on System, Man & Cybernetics

We proposed a complex discrete wavelet transform (CDWT), which uses real-imaginary spline wavelet (RI-Spline wavwlet). In this study, we will extend the 1-D CDWT to the 2-D CDWT, which will be applied to image processing. The main results obtained can be summarized as follows: 1) The problem of 2-D translation variance which is caused by the DWT using a real mother wavelet. 2) The 2-D CDWT is applied to image processing, and encouraging results are obtained.


Signal Processing Using Translation Invariant RI-Spline Wavelet

IEEE International Conference on Systems, Man & Cybernetics

De-noising using wavelet shrinkage has a translation variance problem which we solve by using the translation invariant DWT. For this purpose, we propose a new complex wavelet, the real-imaginary spline wavelet. We also propose the Coherent Dual-Tree algorithm for the RI-Spline wavelet without increasing the computational cost very much. Then we apply this translation invariant RI-Spline wavelet for translation invariant denoising. Experimental results show that our method, when applied to ECG data and a music signal, can obtain better de-noising result than Wavelet Shrinkage.


Acoustic model adaptation for coded speech using synthetic speech

Proc. of International Conference on Spoken Language Processing

In this paper, we describe a novel acoustic model adaptation technique which generates speaker-independent HMM for the target environment. 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. The proposed method uses HMM-based speech synthesis to generate adaptation data from the acoustic model of HMM-based speech recognizer, and consequently does not require any speech data for adaptation. Experimental results on G.723.1 coded speech recognition show that the proposed method improves speech recognition performance. A relative word error rate reduction of approximately 12% was observed.


Distributed Speaker Recognition using Earth Mover's Distance

In this paper, we focus on distributed speaker recognition, a technique in which quantized feature parameters are sent to a server, as with distributed speech recognition. The Gaussian mixture model , the traditional method used for speaker recognition, is trained using the maximum likelihood approach. The GMM has output probability functions with continuous density functions. It is difficult to fit continuous density functions to quantized data. To overcome this problem, we propose a novel speaker recognition technique which does not need estimation of statistical parameters. The proposed method directly calculates the distance between a set of quantized feature parameters of registered speech and a set of quantized feature parameters of test speech. To measure distance, we use Earth Mover's Distance (EMD). The EMD has recently been successfully applied to image retrieval. We conduct text-independent speaker identification experiments using the proposed method. When compared to results using the traditional GMM, the proposed method yielded relative error reductions of 75% (at 16kHz sampling) for quantized data.


A Super-Function Based Japanese-Chinese Machine Translation System

6th Conference of the Association for Machine Translation in the Americas

In this paper, a Japanese-Chinese Machine Translation (MT) system using the so-called Super-Function (SF) approach is presented. A SF is a functional relation mapping sentences from one language to another. The core of the system uses the SF approach to translate without going through syntactic and semantic analysis as many MT systems usually do. Our work focuses on business users for whom MT often is a great help if they need an immediate idea of the content of texts like e-mail messages, reports, web pages, or business letters. In this paper, we aim at performing MT between Japanese and Chinese to translate business letters by the SF based technique.


A Packet Loss Concealment Algorithm using Speech Recognition and Synthesis

Proc. of the 18th International Congress on Acoustics

In recent years, IP telephone use has spread rapidly thanks to the development of VoIP (Voice over IP) technology. However, an unavoidable problem of the IP telephone is deterioration of speech due to packet loss, which often occurs on the wireless network. To conceal packet loss, modern codecs (e.g. G.711, G.723.1, G.729) derive the parameters for the lost frame from the parameters of previous frames. However, these methods cannot deal with losses more than 60 ms in length (in G.711), and cannot fundamentally deal with the loss of more than two phonemes. To overcome this problem, we propose a novel packet loss concealment algorithm using speech recognition and synthesis. First, using speech recognition based on Missing Feature Theory the phoneme-state sequence of the missing frames is presumed using acoustic information and linguistic information before and after the missing frames. Next, HMM-based speech synthesis generates a synthetic speech signal according to the presumed phoneme-state sequence to replace the missing data. The proposed method uses linguistic information and can deal with the lack of syllable units which conventional methods are unable to handle. We conducted subjective and objective evaluation experiments. For the subjective evaluation experiment, we conducted paired comparison auditory tests using 11 testees to examine the ease of understanding speech both lacking a section and having the synthesized replacement patch. Results show the proposed method improved perceived speech quality under all conditions. For the objective evaluation experiment, we measured speech quality using PESQ (ITU-T Recommendation P.862). The average PESQ value of the speech recovered by the proposed method was 3.37, while the value of the speech with a missing section was 2.78. Although there is a processing delay in the proposed method, we believe that the method will open up new applications for speech recognition and speech synthesis technology.


Internet Archive as a Source of Bilingual Dictionary

Proc. of IEEE International Conference on Information Technology

Parallel corpus is a very important tool to construct a good machine translation system or make any natural language processing research for cross language information retrieval. Internet archive is a good source of parallel documents in different languages. In order to construct a good parallel corpus from the Internet archive, Bilingual dictionary that contains word pairs which may not exist in commercial dictionaries is a must. Extracting a bilingual dictionary from the internet parallel documents is important to add words that are absent from the traditional dictionaries. This paper describes two algorithms to automatically extract an English/ Arabic bilingual dictionary from parallel texts that exist in the Internet archive. The system should preferably be useful for many different language pairs. Like most of the systems done, the accuracy of our system is directly proportional to the amount of sentence pairs used. By controlling the system parameters, we could achieve 100% precision for the output bilingual dictionary, but the size of the dictionary will be smaller.


Automatic estimation of stock market forecasting and generating

Time-series forecasting is an important research area in several domains. Recently, neural networks have been very successfully applied in time series to improve multivariate prediction ability. Several neural network models have already been developed for the market prediction. Some are applied to predicting the change of future interest rate and exchange rate; some are applied to recognizing certain price patterns that are characteristic of future price changes. This paper presents a neural network model for technical analysis of stock market, and its application to a buying and selling timing prediction system for stock index of Japan. This paper also describes a natural language generation system to express prediction information of TOPIX in natural language for non-expert users. This system has evolved to be one of the most comprehensive grammars of English for prediction expressions.


An Algorithm for Determining DingYu Structural using Grammar Knowledge

Fifth International Conference on Intelligent Text Processing and Computational Linguistics

Current machine translation system lacks the ability for resolving the problem mentioned above. To resolve this problem, this paper presents an algorithm for determining DingYu structural particle using the grammar knowledge and statistical information. We first collect a large number of grammar items from Chinese grammar books, and obtain some elementary judgment rules by classifying and inducing the collected grammar items. Then we put these judgment rules into use in actual Chinese language, and modify the rules by checking their results instantly. Lastly we check and modify the rules by using the statistical information from a actual corpus. An experiment system based on the proposed algorithm has been constructed and an experiment is carried out. The result shows the effectiveness of the presented method.


English/Arabic Bilingual Dictionary Construction using Parallel Texts from the Internet Archive

Proc. of the 46th IEEE International Midwest Symposium on Circuits and Systems

In order to construct a good machine translation system or make any natural language processing research for cross language information retrieval you must have a good parallel corpus. Internet archive contains a lot of parallel documents. To construct a good parallel corpus from the Internet archive, you must have a good bilingual dictionary. This paper describes an algorithm to automatically extract an English/ Arabic bilingual dictionary from parallel texts that exist in the Internet archive. The system should preferably be useful for many different language pairs. Unlike most of the systems done, our system can 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 other and the explanation of the Arabic or English word in the other language as well. The accuracy of the system is 59.1% in the case of one English word translated to one Arabic word, 23.9% in the case of one English word translated to more than one Arabic word (Arabic phrase), and 14.6% in the case of one Arabic word translated to more than one English word (English phrase).


Pragmatic Approach in Natural Language Understanding

IEEE International Conference on Natural Language Processing and Knowledge Engineering

Natural languages that we use in daily conversation inevitably omit or insert various words. Incomplete sentences such as these may be grammatically correct, but they prevent machine translation systems from correctly interpreting such sentences as they assume meanings only from sentence-to-sentence context. This is largely due to the fact that context is a social product defined by the participants of a party, while translation machines literally translate the information provided by the speakers. The authors of this paper confirmed the effectiveness of syntactic constraint-lead approaches to Collaborative Fusion Machine Translation (CFMT) based on group Rorschach tests, in addition to semantic constraint-lead and syntactic constraint-lead machine translation processes.


Researches on the emotion measurement system

IEEE International Conference on System, Man & Cybernetics

In the near future, it will be necessary for the senior citizens to nurse the other senior citizens because of declining population of children and increasing a new type of family. We have been developing welfare robots which can support lives of the senior citizens and have sensibility to lighten the burden imposed on nursing. The measurement of emotions from the usual conversation is considered to be one of its basic researches. In the paper, we are going to propose the algorithm of the emotion measurement and the prototype system based on this algorism, and we are also going to discuss its validity.


Automatic Evaluation of Sentence Fluency

In the machine translation (MT) system, how to evaluate the sentence fluency of the translation results is an important research topic. Most of the current methods are based on the similarity of output words compared with the reference translation, which don't specially address the evaluation of the sentence fluency according to the syntactic structure. This paper proposes a statistical approach to the problem, which is based on the n-gram language model and reference-independent. Our approach is a beneficial compensation to the reference-dependent methods and has better robustness than those methods based on the syntactic analysis. The preliminary experimental results indicate that the approach basically reflects the reality of human's judgment.


Machine Translation method using super-function for Mobile Terminal

The mobile telephone etc is the representation of mobile communication. In this paper we proposed a new machine translation method called Super-Function Based Machine Translation (SFBMT) to try to use for mobile terminal. SFBMT uses Super Function (SF) to translate the sentence without thorough syntactic and semantic analysis. And it can translate the sentence fast and high correct. Our experiment shows that this method can get 70% translation precision using the sentences that learned for extraction Super-Function, and 40% translation precision using the sentences that not learned for extracting Super-Function.


The Sensibility Inference Function by Psycho-Quantum Computer

Human cognition stems from his identifying the relation or relative distance between himself and any other objects or persons, for which prerequisite is establishing his `self'. In other words, it is presumable that the potential of intelligent machine's establishing its `self' would require us to engineer its sense of `ego' and `intention', which involves such factors as `emotions', `instincts', and `sensibilities'. This dissertation introduces the psycho-quantum theory which illustrates the human psychodynamics by the quantum mechanics approach, and its application for constructing the distribution model of emotional modalities and the autonomous system, the essential elements for creating an ego in a computer. We have attempted to create the artificial `ego' and `desire' based on our probability model and state transition analysis, then accordingly developed our methodology of the artificial thought process as well as the judging mechanism of such computer. Furthermore, this dissertation introduces the sensibility inference function of our psycho-quantum computer, by which we discuss our methods to itemize 126emotional modalities of human, construct the three-dimensional dynamic model of emotions, and analyze the emotion distribution and its longitudinal modal transition so as to derive the artificial instinct and to determine/infer its relationship with external factors. Currently, our technology to construct the comprehensive psycho-quantum computer is yet in developmental stage; however, its potential is enormous so that it will constitute a key technology in new robotics industry.


Blind equalization via minimization of VQ distortion for ETSI standard DSR front-end

Proceedings of Natural Language Processing and Knowledge Engineering

In this study we present blind equalization techniques for ETSI standard Distributed Speech Recognition (DSR) front-end which compensate for acoustic mismatch caused by input devices. The DSR front-end employs vector quantization (VQ) for feature parameter compression so that the mismatch does not only cause a shift of parameters but also increases VQ distortion. Although CMS is one of the most effective methods to compensate for the shift, it can not decrease VQ distortion in DSR. To compensate for the shift and decrease VQ distortion simultaneously, the proposed methods estimate the shift in the input data necessary to match the VQ codebook distribution. The methods do not need the acoustic likelihood which is calculated in a decoder on the server side. Therefore, they are applicable to the DSR front-end. Japanese Newspaper Article Sentences database (JNAS) was used for the equalization experiments. While the word error rate (WER) for ETSI standard DSR front-end was 18.6% under acoustic mismatched condition, our propsed method yielded a rate of 12.3%.


Super-Function based Japanese-English machine translation

We have presented a new method called Super-Function Based Machine Translation (SFBMT). SFBMT uses Super-Function (SF) to translate without syntactic and semantic analysis as most conventional MT systems do. SF represents correspondence with a source language sentence structure and a target language sentence structure directory. According to this feature, the system realizes very fast translation. Moreover we do not feel the sense of incompatibility in the translation result because the SFs are automatically generate from a large corpus. In this paper, we describe (1) SF based machine translation, (2) extracting SF, (3) the evaluation experiment. As the experimental results, we obtain 84% of effective rates of translation.


An acoustic model adaptation using HMM-based speech synthesis

Recently, personal digital assistants like cellular phones are shifting to the IP terminal. The encoding-decoding process utilized for transmitting over IP networks deteriorates the quality of the speech data. This deterioration causes degradation in speech recognition performance. Acoustic model adaptations could improve recognition performance. However, the current adaptation methods usually require a large amount of adaptation data. A novel adaptation method using speech synthesis based on HMM (Hidden Markov Model) is proposed. This method does not require speech data for adaptation because speech data is generated by speech synthesis from the acoustic model. Experimental results on G.723.1 coded speech recognition show that the proposed method improves speech recognition performance.


An XML-based Platform-Independent Model-Level Interface fro Database Applications

IEEE International Conference on Systems, Man and Cybernetics

In this paper, we present a platform-independent model-level interface for database applications. This is an XML-based meta-protocol which, when combined with application-specific metadata, produces a protocol that supports complete interaction with a database. Specification of the metadata at the data modeling level eliminates any dependence on the nature of the underlying database platform. This paper focuses on the database query and response elements of the protocol.


To define the feature function in extracting Japanese-Chinese bilingual word pairs using maximum entropy modeling

In this paper, we present an approach to construct a translation dictionary used in multilingual natural language processing such as machine translation. We use a learning and extracting method of bilingual word pairs from parallel corpora using the maximum entropy modeling. We have conducted the experiments in Japanese and Chinese parallel corpora.


Machine-Aided Writing Function in MMM Intelligent System

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-functions Multi-media Intelligent System. In this paper, we describes a new function, Machine-Aided Writing Functions, in the MMM system. Our methodology proposed in this paper can deal with any foreign languages, but we only focus on how to construct a Machine-Aided English Writing system for Japanese users in the paper. 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 not only on spelling checking and grammar checking but also on writing native sentences using the expression way of native-English 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 presented to develop multi-lingual machine translation. A prototype Machine Translation 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. In this paper we will discuss the SF and CIT in detail and give some new advance in MMM project.


Semi-Automatic Acquisition Translation Knowledge from Parallel Corpora

Translation in most current machine translation systems is based on rules. a crucial problem in such rule-based machine translation is how to acquiring the translation knowledge, or called translation rules. many studies have been conducted for automatic acquisition in the past, but they require a great deal of annotated examples. however, it is difficult to get a large number of the annotated examples. at fact, most bilingual texts are not matched in sentences, although matched as the whole text. in this paper, we describe a semi-automatic process of acquisition machine translation knowledge from japanese-chinese parallel corpora. the process consists of the following three parts. the first part is parallel text alignment, pta for short. the second part is example analysis and annotation, eaa for short. and the third part is construction of the translation rules, ctr for short. the process interacts with a linguist in order to solve problems that the system is unable to solve. this approach is more efficient than the completely automatic approach in the sense that fewer examples are required. in this paper, we describe the basic idea and the methods of pta, eaa, and ctr. a prototype system based on the proposed method has been built and some experiments on japanese- chinese have been carried. the results show that once the acquired knowledge is integrated with an mt system, we observe a signicant improvement in translation quality.


Sentience System Computer: Principles and Practices

Research on artificial intelligence and sensitivity control technology progresses in recent years, and the technology of imitation human thinking, feeling, and sensitivity have been established. However, research on Sentience as the way of idea generation and intuition that are the origin of human being's intellectual production activity seems to have just started. Moreover, although the field of quantum computer is also being researched briskly, no practical results have been produced yet. In this paper, a new machine called Sentience System Computer (SSC) is presented. SSC is an intuition computer system having self-intention. According to the interpretation in the many-words quantum theory, the spontaneous intuition of human can be defined as what is produced by interference from the self multiple worlds which are resulted from all piles of self-history. In this paper, we present the principles and practices of SSC, the basic spontaneity type algorithm. When applying SSC to sensitivity control technique, the process of generating sentience, the procedure of human conceives, the state of Idea Database and some recent results of SSC, are given also.


Long-term Relevance Feedback using Simple PCA and Linear Transformation

13th International conference and workshop on Database and Expert Systems Applications

This paper proposes a new method to improve information retrieval performance of the vector space model by preserving user-supplied relevance information for a long term in the system.


Robust Feature Extraction in a Variety of Input Devices on the Basis of ETSI Standard DSR Front-end

7th International Conference on Spoken Language Processing

This paper reports an evaluation ETSI standard DSR (Distributed Speech Recognition) front-end through continuous word recognition on a Japanese speech corpus and proposes a method, the basis removal method, that reduces the distortion between feature vector and VQ codebook.


Annotating the functional chunks in Chinese sentences

中国語文の機能チャンクの自動注釈

In Proceedings of the Third International Conference on Language Resources and Evaluation

The paper proposed a new syntactic annotation scheme functional chunk, which tried to represent information about grammatical relations between sentence-level predicates and their arguments. Under this scheme, we built a Chinese chunk bank with about two million Chinese characters, and developed some learned models for automatically annotating fresh text with functional chunks. We also proposed a two-stages approach to build Chinese tree bank on the top of chunk bank, and gave some experimental results of chunk-based syntactic parser to show the advantage of functional chunk for parsing performance increase. All these work lays good foundations for further research project to build a large scale Chinese tree bank.

本論文は，新しい構文的な情報付けスキームを提案する．これを機能塊と呼び，文レベル間の文法関係に関する情報を表すものである．本論文ではこのような新しい概念を述べ，いろいろな検討を行う． このスキームの下では，我々は200万漢字の機能塊を構築した．そして自動的な学習モデルを提案した．さらに，我々はこのような機能塊ベースを利用し，中国語木バンクを建てるために2-段階アプローチを提案した．実験結果により，提案した方法の有効性を確かめることができた．


The Ideal Computer: Understanding and Creating the Emotion and Sensitivity

理想的なコンピュータ:感性と感情の理解と創生

Proceedings of China-Japan Joint Symposium on Science and Technology in the 21st Century

On the basis of considering Sentience as a concrete algorithm and equipment, we have conducted the research on the principle of the many-worlds interpretation for the quantum theory of astrophysics. In this paper, a new machine called Sentience System Computer (SSC) is presented. SSC is an intuition the interpretation in the many-worlds quantum theory, the spontaneous intuition of human can be defined as what is produced by interference from the self multiple worlds which are resulted from all piles of self-history. If the total of the interferences from the multiple worlds other than human self is already fixed (based on the law of mass preservation, and the law of energy preservation), it will be possible to control the interferences. Consequently, the basic concept of SSC is drawn from the idea that the cause of the way of thinking of a machine and intuition in SSC become reproducible by the quantum theory-algorithm. We present the principles and practices of SSC, the basic spontaneity type algorithm. When applying SSC to sensitivity control technique, the process of generating sentience, the procedure of human conceives, the state of Idea Database and some recent results of SSC , are given also.

近年の人工知能と感性制御技術の進歩に従って，人間思考，感覚および感情を模倣する技術が確立された．しかしながら，人間の知的生産活動を制御するメカニズムを明らかにしていない．量子コンピュータ分野でいろいろと研究が活発になされているが，実際的結果はまだ生まれていない．本論文では，感情システムコンピュータ(SSC)と呼ばれる新しいマシンの概念を提案する．


Japanese-Chinese Machine Translation System Using SFBMT

SFBMTを利用した日中機械翻訳システム

International Conference on Information-2002, Series of Information & Management Sciences

Information circulation between international has come to be frequently performed with social high advancement in information technology, and the necessity for machine translation has been increasing quickly. In fact most users prefer that the system is fast, inexpensive, easy to control and easy to update. This means that we don't need high fluency of the translations but a useful translation system. In recent years, in order to realize a highly precise machine translation system, many machine translation systems have been put in practical use. Such conventional research aims at raise the accuracy and fluency of a translation sentence but there are many problems. In this paper, a Japanese-Chinese machine translation method using Super-Function (SFBMT) is presented. SFBMT uses Super-Function to translate without through syntactic and semantic analysis at most MT systems usually do. The SF is a function that shows the correspondence between original language sentence and target language sentence. In this paper, we aim to perform the Machine Translation between Japanese and Chinese by the technique.

情報技術の高度的な発展により国際間の情報循環は頻繁に行われており，機械翻訳対するの需要が著しく増加した．利用者は高速に，かつ自然性の高い翻訳結果を得られる有効的な翻訳システムを求めている．近年，高精度的な機械翻訳システムを実現するために , 多くの機械翻訳システムが実用化された．それら従来のシステムには翻訳精度および自然性の面でまだまだ多くの問題がある．本稿では，スーパー関数に基づく日中機械翻訳手法を提案する．提案するシステムではスーパー関数を利用して構文解析および意味解析を行わずに翻訳することが出来る．また，コーパスベースで翻訳を行うため自然な翻訳結果を得られるという利点もある．本稿では，この手法によって構築した日中機械翻訳について述べる．


Machine-Aided English Writing Prototype System

機械支援英語ライティングプロトタイプシステム

The chance to make the writing English increases by the internationalization. However, writing English is a big barrier for most Japanese users. The machine translation is enumerated as typical support method of the writing English. It is possible in a large amount of translation short time. However, the machine translation cannot translate any sentences, and it is difficult to generate native-English. In this paper, we propose an approach to Writing English support system for Japanese users. The approach is based on examples. The example presentation type consists of two components: 1) Support by English sentence classification, and 2) Support by use of Super-Function. Both components work together in a unified way and improve the quality of presentation examples. We also developed English writing support tool of prototype. The writing English support system was evaluated with this tool, and it worked on the system development.

国際化の進展に伴い英語文章を作成するの機会が増えている． しかしながら，英語に手紙を書くことはほとんどの日本人とって大きな障壁である． この問題に対し，機械翻訳への期待は非常に大きいが，未だ技術的には未完成な部分が多く，翻訳結果をそのまま利用することは困難となっている． この問題に対し我々は，日本人のための新しい英語作文支援システムを提案します．提案するシステムは，構造化された対訳英文集と考えることができるが，さらに，コーパスに基づく翻訳手法として有効なSuper Functionを利用することで，文章部品の選択および名詞の自動置き換えにより英作文が可能となる．


Automatic Map-Generation Based on Verb Frames

動詞フレームに基づく自動地図生成

In this paper, we present a method to generate a map from natural language. First of all, to generate the guide map, the guide sentence was divided into named ``Guidance Unit''. It is fundamental division unit punctuated with each morpheme that is independence verb. By using this unit, irrelevant expressions can be excluded from the analysis, and also more flexible expressions can be taken into account. Next, after the guidance unit had been made verb frames, spatial information was extracted. After that, the guide map was generated based on proposed method has been built and some experiments have been carried out. The experimental result shows the effectiveness of the proposed methods.

本論文では，音声等の自然言語により説明される道順(道案内文)から道案内図を生成する手法を提案する．まず，道案内文を「案内ユニット」と呼ぶ，説明のためのプリミティブに分割する．「案内ユニット」を単位とすることで，道案内に関係しない表現を除外する．作成された「案内ユニット」ごとに動詞フレームを構成し，道案内図に必要なオブジェクトを取り出す．オブジェクトと対応する動詞から道順を表す案内図を作成する．1km程度の距離にある目的地への道案内文を用いた実験の結果，提案手法の有効性を確認した．


Japanese-English Machine Translation Systems for Web Users Using Super-Function

スーパー関数を使用したWebユーザーのための日英機械翻訳システム

We have presented a new method called Super-Function Based Machine Translation (SFBMT). SFBMT uses Super-Function (SF) to translate without thorough syntactic and semantic analysis as most MT systems do. This translation speed is very fast. And more we do not feel the sense of incompatibility in the translation result because SF is a corpus base. For a fact use of the Internet increases, more and more necessary information is often written in foreign languages. Web user hopes fast translation and easy control systems. SFBMT is best for their hopes.

近年，インターネットの普及に伴って，外国語の文章を目にする機会が増えている．そこでオンラインで手軽に利用できる翻訳システムの構築を目指して Super Function (SF) を用いた翻訳システムを提案する．SFは原言語から目的言語への変換を従来の概念を超えた関数とみなし，パターンマッチングと置き換え処理により翻訳を実現する．その結果，従来のほとんどの機械翻訳システムが必要としていた構文解析や意味解析を必要せず，高速な翻訳を少ない計算量で実現することが可能となる．本稿では，同手法を英日機械翻訳に適用した結果について報告する．


The Influence of Speech Coders for IP telephone on Speech Recognition Performance

音声認識の性能上のIP電話のためのスピーチコーダーの影響

For transmission over IP networks, speech must be encoded at the sending end and subsequently decoded at the receiving end. In this encoding-decoding process, some speech information has to be removed and it is natural that the performance of speech recognition systems will deteriorate when the coded speech is applied as input to a recognition system. This paper reports an evaluation of G.723.1, one of the speech coders for IP telephone through continuous word recognition on Japanese speech corpus. Experimental results show that the recognition performance of G.723.1 coded speech is degraded compared to no coded speech. In this paper, we investigate these results.

インターネット上で音声通信を実現する場合，一般的には音声の圧縮が行われる．圧縮するとき，聴覚的に音声が劣化しないよう様々な工夫がなされるが，それらの音声を自動音声認識により認識した場合，認識率が低下することが予想される．そこで，本稿では，IP電話で最も低ビットレートのG.723.1を対象に日本語の連続音声認識実験を行いコーデックが音声認識に与える影響を調査する．さらに，コーデックを通した音声で音響モデルを再構成し，認識率の改善を試みるとともに，その限界を調査する．


Latent Semantic Indexing Based on Simple Principal Component Analysis

In this paper, we propose to use the Simple Principal Component Analysis (SPCA) for dimensionality reduction of the vector space information retrieval model. The SPCA algorithm is a data-oriented fast method which does not require the computation of the variance-covariance matrix. In SPCA principal components are estimated iteratively so we also propose a criteria to determine the convergence. The optimum number of iterations for each principal component can be detemined using the criteria. Experimentally, we show that the SPCA-based method offers improvement over the conventional SVD-based method despite its small amount of computation.


An Evaluation of Japanese Speech Recognition Using ETSI Standard DSR Front-end

This paper reports an evaluation of ETSI standard DSR (Distributed Speech Recognition) front-end through continuous word recognition on a Japanese speech corpus. Experimental results show that using non-quantized features in acoustic model training procedure can improve the recognition performance of DSR front-end features.


Extracting Japanese-Chinese Bilingual Word Paris with Maximum Entropy Model

Series of Information & Management Sciences

In this paper,, we present an approach to construct a translation dictionary used in multilingual natural language processing such as machine translation. We use a learning and extracting method of bilingual word pairs from parallel corpora using the maximum entropy modeling. We are doing the experiment in Japanese and Chinese parallel corpora.


Sensitive Word Concept and Its Application in Chinese Text Segmentation

敏感語概念とその中国語テキスト分割への応用

ICCC2001,International Conference of Chinese Computing, Chinese Language e-Learning,

The identification of words in Indo-European languages is a trivial task. However, this 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 text. 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.

インドヨーロッパ語について単語の識別は重要でないタスクであるが，テキストの単語識別，即ち単語区分という問題は，中国語のようなアジアの言語処理におけるボトルネックである．中国単語の識別では主に二つのアプローチがある．即ち，辞書に基づくアプローチおよび統計に基づくアプローチである．しかしながら，二つのアプローチもある中国語テキストに対処できないことがある．このような障害を克服するために，我々は中国語に対して敏感語概念を提案し，この概念を利用して，ハイブリッドアプローチを提案する．本論文では，テキスト単語識別における敏感語概念およびそれらの効能について述べる．次に，敏感語概念を使用する規則ベースの方法および統計ベースの方法を組み合わせるハイブリッドアプローチについて述べる．実験結果は，提案したアプローチがテキスト識別に有効できることを示した


Automatic Abstracting Important Sentences of Web Articles

IEEE International Conference on System, Man, and Cybernetics

Being increasingly popular, the Internet greatly changes our live. We can conveniently receive and send information via the Internet. With the information explosion in Web, it is becoming crucial to develop means to automatically extract important sentences from the Web articles. In this paper, we propose a method which uses both statistical and structural information in sentence extraction. In addition, following the analysis of human's extractions, several heuristic rules are added to filter out non-important sentences and to prevent similar sentences from being extracted. Our experimental results proved the effectiveness of these means. In particular, once the heuristic rules being added, a significant improvement has been observed.


A New Machine Translation Approach using Multiple Translation Engines and Sentence Partitioning

複数の翻訳エンジンと文パーティショニングを活かした新しい機械翻訳アプローチ

In this paper, we present a new machine translation (MT) approach using MT engines and sentence partitioning. A multiple engine MT system consists of several MT engines running in parallel, coordinated by a controller. Each engine is implemented using an existing MT technique and has its own characteristics. When translating a sentence, each engine translates it independently. If more than one engine translates the sentence successfully, the controller chooses the best translation according to a combining algorithm implemented using translation statistics. If no engine succeeds in translating the sentence, the controller partitions the sentence, coordinates the engines to translate its constituent simple sentences, and combines the partial translation results into a translation result for the whole input sentence. A complex sentence is partitioned based on conjunctives and punctuation marks such as comma and semicolon. We have developed a multiple engine MT system based on the above approach. The system consists of four independent MT engines. The experiments show that the proposed approach is effective for implementing practical MT systems.

本論文では，複数翻訳エンジンと文の分割に基づく新しい機械翻訳(MT)アプローチを提案する．複数の翻訳エンジンを持つMTシステムは，平行運行の複数翻訳エンジンからなるが，コントローラによって調整される．各々のエンジンはそれぞれ既存の機械翻訳技術を使用して，インプリメントされ，それ自身の特性がある．文を翻訳する場合，エンジンはそれぞれ独立して翻訳する．複数の翻訳エンジンが文を成功裡に翻訳する場合，コントローラは，統計情報に基づくアルゴリズムにより調整する．どのエンジンも成功しない場合，コントローラは文を分割し，その構成する単文を翻訳するためにエンジンを調整し，部分的な翻訳結果を組み合わせ文全体の訳文を生成する．


A Hybrid Approach to Automatic Chinese Text Checking and Error Correction

中国語テキストのチェックとエラー訂正のハイブリッド手法

Automatic Chinese text checking and error correction is an important and difficult problem. Compared with automatic checking and error correction of Western text, automatic checking and error correction of Chinese text faces more challenges. The Chinese language has many characters and no delimiters separating words. It is impossible to detect and correct errors by penetrating into the inner composition of a character. In this paper, we describe some special features of Chinese characters and text and some statistical information obtained from a real world Chinese text corpus, and we present a hybrid approach that combines a rule-based method and a probability-based method to automatic checking and error correction of Chinese text. We also present an experimental system, HSACCCT (Hybrid System of Automatic Checking and Correction for Chinese Text), that implements this hybrid approach and some experimental results on real world Chinese text.

中国語テキストの自動チェックおよびエラー訂正は重要で難しい問題である．西洋語テキストの自動チェックおよびエラー訂正と比較して，中国のテキスト自動チェックおよびエラー訂正はより多くの挑戦に直面する．本論文では，漢字とテキスト特徴を検討し，実世界における中国語テキスト·コーパスから得られたある統計資料に基づき，新しい手法を提案する．これは中国語テキストの自動チェックおよびエラー訂正への規則ベースの方法および統計ベースの方法を組み合わせるハイブリッドアプローチである．


Using Natural Language to Access Databases on the Web

自然言語を使用したWeb上のデータベースアクセス

As the traditional relational databases and the new XML document repositories are being widely used on the Web as information storage, there is a great need for easy access to the information sources, particularly through natural language interactions. In this paper, we present the design and implementation of an intelligent system that interacts with users using a natural language, English, and retrieves information from sources, for the users. The system consists of four major parts: part of speech tagging, query knowledge base, query formation, and answer synthesis. In implementation, the system first uses QTAG, a Hidden Markov Model based speech part tagger, to tag each word in the input sentence. Then, important words in the main phrase are identified. A thesaurus is applied to reduce the important words to basic keywords, which are used to query the database. The query is formed based on the query knowledge stored in the query knowledge base. Finally, the query result is synthesized into an English sentence, which is presented to the user as the answer. With an efficient part of speech tagger, intelligent subsystems for query formation and synthesis of query result, and user-friendly interface, the intelligent system can answer questions effectively.

従来のリレーショナル·データベースおよび新しいXMLドキュメント·リポジトリが情報記憶としてウェブ上で広く使用されているとともに，特に自然言語によって，情報をアクセスする必要がある．本論文では，ユーザのために，自然言語(英語)を使用して，ユーザと対話し，出所から情報を検索するインテリジェントシステムのデザインおよびインプリメンテーションを述べる．システムは4つの大部分からなる．品詞タグ付け，クエリー知識ベース，質問形式化および答え合成．インプリメンテーションでは，システムは，入力文での各単語に札を付けるためにQTAG(隠されたマルコフ·モデルベースのスピーチ部分タグ付け)を使う．その後，主な句中の重要な言葉は識別される．クエリーは，クエリー知識ベースに格納されたクエリー知識に基づいて構成される．最後に，クエリー結果は英語文を合成する．それは答えとしてユーザに提示される．効率的な品詞タグ付け機，クエリー結果の質問形式化および合成用の知的なサブシステムおよびユーザ·フレンドリーなインタフェースで，インテリジェントシステムは質問に有効に答えることができる．


New Machine Translation Paradigm: Parallel Translation

新しい機械翻訳パラダイム:平行翻訳

機械翻訳では一番難し問題はいろいろな曖昧性の処理である．例えば，形態素解析における曖昧性，単語の曖昧性，品詞の曖昧性，文構造の曖昧性，意味構造の曖昧性などが挙げられる．本論文ではこのような核心の問題を解決するため，並列翻訳パラダイムを提案する．また，並列機械翻訳の原理，概念，そして実用手法を述べる．