An Improved Method of Extracting Emotional Attribute of Relations Based on Syntactical Analysis

Proc. of IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS 2011)

Social network plays an important role in thisnetwork age. With development of social network,people hope more attribute and more precisedescription about relation which is the key elementof network. This paper proposes an improvedmethod of extracting emotional attribute ofrelations from Chinese events based on syntacticalanalysis. In our work, we improved the defect ofprevious method that can not deal with multipleentities in one sentence. In addition, a large-scaleChinese emotional dictionary not only emotionalverbs was used in the extraction of emotionalattribute. Experiment showed our method achievedsatisfied performance.


PARAGRAPH ACT BASED PRAGMATIC INFORMATION EXTRACTION IN QUESTION ANSWERING

In Question Answering System the greatesthardship is crossing the gap between the questionsand corpus to find the appropriate answers. Thequestions and corpus are in different context, tofind the answers the inference whether onecandidate sentence fits the user's request isnecessary. Pragmatic is concerned with inferenceabout the utterances and user. It is a bridge acrossthe gap of questions and corpus. In this paper weanalyze the elements of pragmatic and extract thepragmatic information from the corpus based onthe paragraph act. The pragmatic information isalso well organized. And the experiments showthat the appended pragmatic informationsignificantly improves the performance of theQuestion Answering System either the precision,recall or F score especially in the condition that thequantity of answer is restricted.


CREATING A CHINESE EMOTION LEXICON BASED ON CORPUS REN-CECPS

As a basic emotion resource, an emotion lexiconplays a very important role in the classification andrecognition of emotion in text. This paper proposesan automatic approach to create a Chinese emotionlexicon with tag of emotion intensity based on theemotion corpus Ren-CECps 1.0. We present a newalgorithm of emotion vector computation bymaking use of two language dictionaries TongyiciCilin and HowNet. Comparing with theexperimental results on verbs by SVMclassification method, our approach is provedfeasible and effective.


DETECT AND TRACK THE DYNAMIC DEFORMATION HUMAN BODY WITH THE ACTIVE SHAPE MODEL MODIFIED BY MOTION VECTORS

At present, many systems are being used in the detection of human body from video, especially the moving human body. These systems usually recognize the human body features, such as learning the human body features by training, recognition of the skin color or body shape, and identify the specifically movement of human body. These methods have some problems. The training methods require a lot of training data, but only identify specific targets. The methods of identify the shape of human body and skin color are a great influenced by the video quality and content. The methods of detecting motion features are often complex and greatly relied to the 3D data. In this study, we propose an Active Shape Model (ASM) modified by the motion vectors to detect and track the deformed human body for action. Active shape model is used to identify the particular shapes; it is also a training method. We use kinds of original human body model modified by motion vectors, instead of training result. Thus the active shape model can detect the human body after deformation. So that human body won't be lost or repeated detected. In this method, we use Optical-flow method to get the motion vectors. The function of the method is conformed by experiment and the detecting success rate of moving human body is 94%. The experimental results and discussion are also presented.


Japanese Named Entity Recognition for Question Answering System

Current question answering (QA) systems usually contain named entity recognizer (NER) as a core component. NER is an important and difficult task in computational linguistics. It plays an important role in natural language processing application such as Question Answering, Machine Translation, and Information Retrieval etc. NER includes the identification and classification of certain proper nouns (like location, organization, person, data, money and others) in a text. The purpose of our study is to recognize and extract the exact Japanese sight seeing domain named entities. It is a basic step for the following processing: question analysis and keyword extraction information retrieval. As well as, through doing the named entity recognition, we consider that it can mine exact information from text document to respond to user. This paper describes how to do the Japanese sightseeing named entity recognition due to we are constructing a Japanese sightseeing question answering system. We adopt the hybrid method which combined with machine learning and rule-base method. In the experiment of Japanese sightseeing domain named entity recognition we have got excellent precision and recalling rates. It shows that our method is effective and can be used in a practical question answering system.


EEG-BASED EMOTION RECOGNITION IN CHINESE EMOTIONAL WORDS

Emotion recognition is becoming a more and more hot topic nowadays. Many researches on facial recognition and speech recognition have been done. In this paper, we develop a method to facilitate emotion recognition with electroencephalographic (EEG) signals. The main objective of our study is to reveal the relation between emotional responses and EEG data gathered through giving seven subjects word-imagination stimuli. To induce emotions we choose 20 Chinese emotional words which have relatively high emotional intensities. We use support vector machine (SVM) and linear discriminant analysis (LDA) to recognize emotion, respectively, the average recognition rates of 48.78% and 57.04% could be achieved.


A novel emotion recognizer from speech using both prosodic and linguistic features

Knowledge-Based and Intelligent Information and Engineering Systems

Emotion recognition based on speech characteristics generally relies on prosodic information. However, utterances with different emotions in speech have similar prosodic features, so it is difficult to recognize emotion by using only prosodic features.In this paper, we propose a novel approach to emotion recognition that considers both prosodic and linguistic features. First, possible emotions are output by clustering-based emotion recognizer, which only uses prosodic features. Then, subtitles given by the speech recognizer are input for another emotion recognizer based on the Association Mechanism. It outputs a possible emotion by using only linguistic information. Lastly, the intersection of the two sets of possible emotions is integrated into the final result.Experimental results showed that the proposed method achieved higher performance than either prosodic- or linguistic-based emotion recognition. In a comparison with manually labeled data, the F-measure was 32.6%. On the other hand, the average of F-measures of labeled data given by other humans was 42.9%. This means that the proposed method performed at 75.9% in relation to human ability.


Construction of Wakamono Kotoba Emotion Dictionary and Its Application

Computational Linguistics and Intelligent Text Processing

Currently, we can find a lot of weblogs written by young people. In the weblogs, they tend to describe their undiluted emotions or opinions. To analyze the emotions of young people and what causes those emotions, our study focuses on the specific Japanese language used among young people, which is called Wakamono Kotoba. The proposed method classifies Wakamono Kotoba into emotion categories based on superficial information and the descriptive texts of the words. Specifically,the method uses literal information used for Wakamono Kotoba, such as Katakana, Hiragana, and Kanji, etc., stroke count, and the difficulty level of the Kanji as features. Then we classified Wakamono Kotoba into emotion categories according to the superficial similarity between the word and the Wakamono Kotoba registered in the dictionary with an annotation of its emotional strength level. We also proposed another method to classify Wakamono Kotoba into emotion categories by using the cooccurrence relation between the words included in the descriptive text of the Wakamono Kotoba and the emotion words included in the existing emotion word dictionary. We constructed the Wakamono Kotoba emotion dictionary based on these two methods. Finally, the applications of the Wakamono Kotoba emotion dictionary are discussed.


An Exploration of Features for Recognizing Word Emotion

Proceedings of the 23rd International Conference on Computational Linguistics

Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and automatic emotion lexicon construction. In this work, we explore features for recognizing word emotion. Based on Ren-CECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, several contextual features and their combination have been experimented. Then PLSA (probabilistic latent semantic analysis) is used to get semantic feature by clustering words and sentences. The experimental results demonstrate the effectiveness of using semantic feature for word emotion recognition. After that, "word emotion components" is proposed to describe the combined basic emotions in a word. A significant performance improvement over contextual and semantic features was observed after adding word emotion components as feature.


Needs and challenges of care robots in nursing care setting: A literature review

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

This study aims to identify needs and challenges of care robot in nursing care setting through an extensive search of the literature. As the result shows, there exists a shortage of information about results of the introduction of care robots, the needs of recipients and care providers, and relevant ethical problems. To advance our research and to introduce care robots into setting, there are so many things to do; consider the application of natural language processing technology by collaborating with researchers in the robotics field, carry out an investigation, extract the needs, clarify ethical problems and seek solutions, conduct the on-site experiment study, and so on.


The present conditions, problems and future direction of the server-controlled clinical pathway system development in psychiatric hospitals

Clinical pathways used today are paper-based, although many different kinds of clinical pathway are used in clinical practice. However, in the development of software for clinical pathway, it is difficult to achieve cooperation between medical experts, who are not used to expressing their ideas and work in words, and system-developers whose medical knowledge is limited. As a consequence, the current situation is that medical practitioners make their own software and use it in their practice. While this may work, it is less than ideal because refinements that may make the software most effective through engineering expertise is not used. Thus, in our research team, the nurse researchers and the engineering researchers cooperated and developed a clinical pathway system. In this paper, the present conditions, problems, and future direction of the server-controlled CP system development in the psychiatric hospitals, is discussed from viewpoint of nursing as a user.


Designing Effective Web Mining-Based Techniques for OOV Translation

Due to a limited coverage of the existing bilingual dictionary, it is often difficult to translate the Out-Of-Vocabulary terms (OOV) in many natural language processing tasks. In this paper, we propose a general cascade mining technique of three steps, it leverages OOV category to optimize the effectiveness of each step. OOV category based expansion policy is suggested to get more relevant mixed-language documents. OOV category based hybrid extraction approach is suggested to perform a robust extraction. A more flexible model combination based on OOV category is also suggested. Moreover, we conducted experiments to evaluate the effectiveness of each step and the overall performance of the mining technique. The experimental results show significantly performance improvement than the existing methods.


Use Relative Weight to Improve the kNN for Unbalanced Text Category

The technology of text category is widely used in natural language processing. As one of best text category algorithms, kNN is very popular used in many applications. Traditional kNN assumes that the distribution of training data is even, however, it is not the case for many situations. When we used kNN in our Topic Detection and Tracking (TDT) system, it did not perform well due to the bias of training data set. To overcome the obstacle caused by data bias, this paper proposes an approach which uses relative weight to adjust the weight of kNN (RWKNN). When evaluated on the data of TDT2 and TDT3 Chinese corpus, RWKNN proves to be robust on unbalanced data and yields better performance than the traditional kNN.


Chinese patent retrieval based on the pragmatic information

In this paper, we propose a novel information retrieval approach based on the pragmatic information for Chinese patents. At present, patent retrieval is becoming more and more important. Not only because patents are always can an important resource in all kinds of field, but patent retrieval save a great deal of time and funds for corporations and researchers. However, with available methods the precision of retrieval results for patents is not very high. What's more, through analyzed the patent documentations we found that except the literal meanings, there are deeper meanings which can be concluded from the patents. Here we call the deeper meanings as pragmatic information. Therefore we established a patent retrieval system to integrate the pragmatic information with classical information retrieval technique to improve the retrieval accuracy. Some experiments using the proposed method have carried out, and the results show that the precision of patent retrieval based on the pragmatic information is higher than the one without using it.


Emotion Analysis in Blogs at Sentence Level Using a Chinese Emotion Corpus

Previous researches for emotional analysis of texts have included a variety of text contents: weblogs, stories, news, text messages, spoken dialogs, and so on. Compared with other text styles, the main characteristics of emotional expressions in blogs are as follows: (1) Highly personal, subjective writing style; (2) New words and expressions are constantly emerging; (3) The integrity and continuity of using language. Using a Chinese emotion corpus (Ren-CECps), in this study, we make an analysis on emotion expressions in blogs at sentence level. Firstly, we separate the sentences into two classes: simple sentences (sentences without negative words, conjunctions, or question mark) and complex sentences (sentences with negative words, conjunctions, or question mark). Then we compare the two classes of sentence on sentence emotion recognition based on emotional words. Furthermore we analysis the following factors for emotion change at sentence level: negative words, conjunctions, punctuation marks, and contextual emotions. At last, we make an hypothesis that the emotional focus of a sentence could be expressed by a certain clause in this sentence, and the experimental results have proved this hypothesis, which showed that selecting the clauses containing emotional focus of a sentence correctly would be helpful to recognize sentence emotions.


Recognition of Abnormal Vibrational Responses of Signposts using the Two-dimensional Geometric Distance and Wilcoxon Test

In expressway companies, workers have been impacting signposts using wooden hammers and estimating the degree of the corrosion by listening to the sound. In order to automate this, we have been developing software that recognizes an abnormal impact vibrational response due to corrosion. This software extracts sonograms from impact vibrational waves using the LPC spectrum analysis, and matches images of the sonogram between a standard and an input impact vibrations using the Two-dimensional Geometric Distance. Then, the software distinguishes the abnormality of the input impact vibration using Wilcoxon rank-sum test. We have measured the impact vibrations of five normal signposts and five abnormal signposts, and carried out the automatic recognition experiments. As a result, the software has recognized correctly in all cases. We have verified the effectiveness of the proposed method.


Bottom Up: Exploring Word Emotions for Chinese Sentence Chief Sentiment Classification

In this paper we demonstrate the effectiveness of employing basic sentiment components for analyzing the chief sentiment of Chinese sentence among nine categories of sentiments (including No emotion). Compared to traditional lexicon based methods, our research explores emotion intensities of words and phrases in an eight dimensional sentiment space as features. An emotion matrix kernel is designed to evaluate inner product of these sentiment features for SVM classification with O(n) time complexity. Experimental result shows our method significantly improves performance of sentiment classification.


Improving Emotion Recognition from Text with Fractionation Training

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

Previous approaches of emotion recognition from text were mostly implemented under keyword-based or learning-based frameworks. However, keyword-based systems are unable to recognize emotion from text with no emotional keywords, and constructing an emotion lexicon is a tough work because of ambiguity in defining all emotional keywords. Completely prior-knowledge-free supervised machine learning methods for emotion recognition also do not perform as well as on some traditional tasks. In this paper, a fractionation training approach is proposed, utilizing the emotion lexicon extracted from an annotated blog emotion corpus to train SVM classifiers. Experimental results show the effectiveness of the proposed approach, and the use of some other experimental design also improves the classification accuracy.


Pragmatic Analysis Based Query Expansion for Chinese Cuisine QA Service System

This paper proposes a query expansion method for cooking question answering system based on pragmatic analysis. In our approach, the results of question analysis is used. The original queries are generated by means of the question subject, then the query terms are expanded based on pragmatic function. When submitting the expended queries to Google search engine to retrieve related passages, we get an overall improvement of 36.2% on the mean average precision.


Maximum Entropy Based Emotion Classification of Chinese Blog Sentences

At present there are increasing studies on the classification of textual emotions. Especially with the rapid developments of Internet technology, classifying blog emotions has become a new research field. In this paper, we classified the sentence emotion using the machine learning method based on the maximum entropy model and the Chinese emotion corpus (Ren-CECps)*. Ren-CECps contains eight basic emotion categories (expect, joy, love, surprise, anxiety, sorrow, hate and anger), which presents us with the opportunity to systematically analyze the complex human emotions. Three features (keywords, POS and intensity) were considered for sentence emotion classification, and three aspect experiments have been carried out: 1) classification of any two emotions, 2) classification of eight emotions, and 3) classification of positive and negative emotions. The highest classification accuracies of the three aspect experiments were 90.62%, 35.66% and 73.96%, respectively.


An Improved Method of Keywords Extraction Based on Short Technology Text

Keywords are the critical resources of information management and retrieval, automatic text classification and clustering. The keywords extraction plays an important role in the process of constructing structured text. Current algorithms of keywords extraction have matured in some ways. However the errors of word segmentation which caused by unknown words have been affected the performance of Chinese keywords extraction, particularly in the field of technological text. In order to solve the problem, this paper proposes an improved method of keywords extraction based on the relationship among words. Experiments show that the proposed method can effectively correct the errors caused by segmentation and improve the performance of keywords extraction, and it can also extend to other areas.


Wisdom Media CAIWA Channel based on Natural Language Interface Agent

Based on the breakthroughs in natural language interface agent CAIWA, web content distribution platform CAIWA Channel has been built for both PC and smatphone clients. The platform is regarded as wisdom media, since it seeks for providing information precisely what users demand, in addition to removing barriers of user interface bottlenecks. Not only recommendations of information to the user based on the past behavior, interest and profile, but also the CAIWA Channel has built-in evolution mechanism by proactively collecting information about the user in a natural manner via conversation with the user. It can accumulate knowledge for better meeting the user's personal requirements, while web contents are organized according to the user's interest. CAIWA Channel is not a mere information search system but a knowledge query system and a human-touch system incorporating emotional reactions.


Flexible English Writing Support Based on Negative-Positive Conversion Method

With development of the recent globalization, thechance to exchange in English increased in the business field.In particular, its necessary to write a thesis and a charterhandwriting in English. Because many Japanese are notused to making English sentence, it is a great burden towrite appropriate sentence in English without any supportfor creating English sentence. In this study we havedeveloped an English composition support system. By thissystem, its to search for the interlinear translation exampleto refer to by database and generate a new sentence byreplacing a noun in the example sentence. In this paper,based on the technique of Super-Function, we propose amethod to convert an affirmative sentence into negativesentence and vice versa to realize more flexible andextensive text conversion.


Wakamono Kotoba Emotion Corpus and Its Application for Emotion Estimation

Proceedings of 2010 International Conference on Advanced Intelligence

In this paper, we describe how we constructed the linguistic resources of Wakamono Kotoba emotion corpus to be used for emotion recognition and explain the features we obtained from statistical analysis. Our Wakamono Kotoba emotion corpus includes Japanese language used by young people and language for expressing emotion. These were mainly gathered from Weblogs that were written by young people from their teens to their twenties.


Needs and changes of robots in nursing care setting: A Literature review

This study aims to identify needs and challenges of care robot in nursing care setting through an extensive search of the literature. As the result shows, there exists a shortage of information about results of the introduction of care robots, the needs of recipients and care providers, and relevant ethical problems. To advance our research and to introduce care robots into setting, there are so many things to do; consider the application of natural language processing technology by collaborating with researchers in the robotics field, carry out an investigation, extract the needs, clarify ethical problems and seek solutions, conduct the on-site experiment study, and so on.


Texture Image Retrieval Based on Gray-Primitive Co-occurrence Matrix

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

The research of texture similarity is very important component of content-based image retrieval system. Firstly the rotation invariance of gray-primitive co-occurrence matrix was proved in this paper, then a new texture image retrieval technique based on gray-primitive co-occurrence matrix was presented. The result of experiment indicates that the algorithm proposed has low computational complexity and certain noise resisting ability.


An HMM-based segment quantizer and its application to low bit rate speech coding

Proc. International Congress on Acoustics

Several speech coding systems employ a segment quantizer instead of a vector quantizer. One of the most important problems is how to construct a segment codebook. In this paper, a new speech coder based on the ML-BEATS is proposed. The ML-BEATS is one of the HMM-based segment quantizer. First, it splits a vector sequence into several sub-sequences, and then these sub-sequences are clustered in order to construct a codebook. Each cluster center is represented by a left-to-right HMM. In the encoding process, input speech is matched with HMMs in the codebook, and then HMM index and duration information are sent to the decoder. In the decoding process, a decoded sequence is generated from HMM parameters by applying the HMM-based speech synthesis method. From the experimental results, the HMM-based speech coder gave 1.13 dB spectral distortion with 5.83 bit/frame. It is 0.11 dB higher spectral distortion than that given by G.729 coder, but bit rate decreased only 32%. In order to consider a shifting problem of LSP dimensions, we also propose a new codebook construction method. Many training vectors are extracted from training samples by shifting dimensions, and all vectors are used for constructing a universal codebook. The universal codebook can deal with any shifted vectors because all possibilities are included in the training data. From the experimental results, the shifted vector method encoded an input speech with very low bit rate, but it gave higher spectral distortions.


A Study on Cross-Language Text Summarization Using Supervised Methods

In this work, we use Hidden Markov Models (HMM), Conditional Random Field (CRF), Gaussian Mixture Models (GMM) and Mathematical Methods of Statistics (MMS) for Chinese and Japanese text summarization. The purpose of this work is to study the applicability of mentioned three trainable models for cross-language text summarization. For model training, we use several training features such as sentence position, sentence centrality, number of Name Entity and so on. For model testing, Chinese on-line news and Japanese news are used as test data which are extracted from web pages. We evaluate each model by measuring the precision at the compression rate 10%, 20% and 30%. MMS is a baseline method. The results show that HMM, CRF and GMM have remarkable increases than MMS on both Chinese and Japanese text summarization by using the same training features. Especially, GMM model make a best performance in all tests.


Towards the Relationship Between Semantic Web and NLP

With the development of Semantic the Web technology, the NLP technology has much broader prospects. This article analyses the fusion degrees between the two technologies based on the survey of relations of them. We explain the relationship between Semantic Web and NLP in two aspects. One is NLP how to support Semantic Web development in Ontology Learning, Ontology Query and Multilingual Ontology Mapping. The other is Semantic Web technologies how to improve NLP results in Information Extraction and Word Sense Disambiguation. We also propose some research challenges for the cooperation between Semantic Web and NLP.


Analysis on Degree Words for Chinese Emotion Expressions Based on Syntactic Parse and Rules

Analysis of emotions in texts has wide-ranging applications. In the analysis of emotional expressions, degree words are important for expressing emotion intensity of emotions. With the support of a large Chinese emotion corpus (Ren-CECps), in this paper, we present analysis on degree words for Chinese emotion expressions based on syntactic parse and rules. At first, Ren-CECps is used to extract the rules of degree words and their modifying elements. Then, we make a contrast analysis on emotion intensities of emotional words with and without modified by degree words. The quantity level for degree words is presented. Based on the extracted rules and quantity degree, a method of sentence parse is used to extract the modifying elements of degree words and to get the emotion intensity of emotional words when they are modified by degree words in sentences.


Chinese Sentence Similarity based on Word Context and Semantic

Sentence similarity computing plays an important role in the nature language processing. Many different methods are proposed to calculate sentence similarity including word, semantic, syntax and so on. In this paper, we proposed a sentence similarity method for travel question answering system by combining the word context information and semantic similarity together. We searched a series of context structures for keywords in a sentence. Experiment has been carried out to show the effectiveness of our method.


Hyponymy Acquisition from Chinese Text by SVM

Hyponymy as one of semantic relation taxonomies provides a fundamental knowledge for natural language processing applications. In this paper, we propose a method for automatically learning hyponymy terms by machine learning technique from text for Chinese. Our method relies on hand-crafted hyponymy patterns, and uses the syntactic features to build a multiple classifier to identify novel hyponymy pairs (hyponym /hypernym or hypernym /hyponym) in a sentence by SVM. Experimental results show that the method is effective in acquiring hyponymy from Chinese free text.


Recognizing Sentence Emotions Based on Polynomial Kernel Method Using Ren-CECps

Emotion recognition on text has wide applications. In this study we propose a method of emotion recognition at sentence level based on a relative large emotion annotation corpus (Ren-CECps). From this corpus, we get the emotion lexicons for the eight basic emotions (expect, joy, love, surprise, anxiety, sorrow, angry and hate). Statistics show that the emotion lexicons derived from Ren-CECps are used more often in real use of language for emotional expressions than HOWNET sentimental lexicons. Kernel methods are state-of-the-art for solving machine learning problems. Polynomial kernel (PK) method is used to compute the similarities between sentences and the eight emotion lexicons. Then the experiential knowledge derived from Ren-CECps is used to recognize whether the eight emotion categories are present in a sentence. This method obtain 62.7% F-measure.


Designing a Japanese Idiom Education Support System for Overseas' Students

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

The Japanese idioms are phrases which two or more words combine and have the different meaning from the original word. Therefore, learning a Japanese idiom is difficult for Japanese language learners. We are developing a Japanese Idiom Education Support System for Overseas' Students to solve this problem. The main purposes of the system are to raise learner's interest of learning idiom, and to teach the right usage of an idiom. The main functions of this system are the function of Japanese idiom retrieval and teaching Japanese idiom. The learner can input the idiom into the system and the system can retrieve and display an explanation of the idiom. And the learner can input the situation into the system and the system can retrieve the idiom. In this paper, the outline of Japanese Idiom Education Support System for Over-sea's Students, the method of Japanese idiom retrieval, and the result of questionnaire is presented. A questionnaire did the survey on attitudes toward educational support system.


Simple Linguistic Processing Effect on Multi-label Emotion Classification

Emotion plays a significant role in human communications in our daily life. With progress in human-machine interface technology, recent research has placed more emphasis on the recognition of emotion reaction. Comparing to some other ideal experimental settings, blog posts online would be respond more to real-world events. And a huge resource of text-based emotion can be found from the World Wide Web nowadays. This paper reports a study to investigate the effectiveness of using SVM (Support Vector Machine) on linguistic features considering emotion keywords and negative words, and classify a collection of blog posts sentences tagged by one or more labels finally. Our results show that individual emotions can be clearly separated by the proposed approach. To the multi-label classification of emotion, it also obtained a higher accuracy rate than the baseline unigram approach using SVM.


Automatic Text Classification using Modified Centroid Classifier

This work proposes an approach to address the problem of inductive bias or model misfit incurred by the centroid classifier assumption to enhance the automatic text classification task. This approach is a trainable classifier, which takes into account tfidf as a text feature. The main idea of the proposed approach is to take advantage of the most similar training errors to the classification model to successively update it based on a certain threshold. The proposed approach is simple to implement and flexible. The proposed approach performance is measured at several threshold values on the Reuters -21578 text categorization test collection. The experimental results show that the proposed approach can improve the performance of centroid classifier.


A proposal for Creating a Chinese Emotion Thesaurus with Tag of Emotion Intensity

In recent years, researchers have begun to pay more attention to the emotion recognition in natural language processing. In order to help this pursuit, this paper proposes a semi-automatic approach to create a Chinese emotion thesaurus with tag of emotion intensity based on two kinds of language resources HowNet and Tongyici Cilin. As a basic emotion resource, the emotion thesaurus should be used to help the classification and recognition of emotion in Chinese. We present a new algorithm of the semantic similarity computation for aiding the emotion intensity computation between words. The experimental results on verbs prove that our approach is feasible and effective.


Answer Generation for Chinese Cuisine QA System

In this paper, we propose an approach on answer generation for cooking question answering system. We first review previous work of question analysis. Then, we give annotation scheme for knowledge database. Finally, we present the answer planning based approach for generate an exact answer in natural language. An evaluation has been conducted on natural language questions and the result shows that the system can satisfy user's demand.


Unknown Place Name Detection base on YamCha for Japanese Guidance QA system

We perform a research about Japanese sightseeing guidance question answering (QA) system. In this domain Web information retrieval approach is always being used. However for the Web information retrieval, Japanese unknown place word detection needs to be considered. Some famous places are known very well, such as "Kinkaku-ji", it can be recognized by morphological analysis. However, the recognition rate of Japanese complex place is so poor. Therefore, we proposed an approach of the Japanese unknown place word detection. We use YamCha, a tool based on support vector machines (SVMs). The goal is that we want to recognize the unknown sightseeing spots word from user's question. In the experiment of Japanese place word detection we have got excellent precision and recalling rates.


The Study on Text Emotional Orientation Based on a Three-dimensional Emotion Space Model

In order to recognize human emotion, a three-dimensional (3D) emotion space model was established and used to identify the text emotional orientation visually and intuitively. An emotion dictionary contains informations of eight basic emotions, was built based on the emotion corpus. By using this emotion dictionary, emotion keywords which behave distributions of 3D structure can be projected into the emotion space. Emotion distributions were transformed into an emotion matrix. By analyzing the emotion matrix, not only binary classification of texts but also multi-emotion attributes can be investigated. The best precision 91% of a binary classification is obtained in this work.


Existential Negative Sentence Translation In Japanese-Chinese Machine Translation

There are lots of negative sentences in both Japanese and Chinese, which are varing from meanings and phrasings. Especially the negative sentences always express complex relationships in Chinese language. So it is easy to make ambiguity when translating Japanese into Chinese. At present, it's very common that the translation softwares sold on the market make errors due to wrong translation of the negative sentences. This paper investigates the difference of existential negative sentences in Chinese and Japanese, then formulates translation rules and comes up with several translation ways for existential negative sentences based on characteristics of Japanese sentences, the corresponding attributes of the terms, the phrasings of Chinese sentences and so on. When testing on 150 negative sentences, the experiment confirms the effectiveness of the method introduced in the paper.


Extended Super Function based Chinese Japanese Machine Translation

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering

Research on machine translation has a long history and many methods and techniques have been proposed and developed. However, low quality of translation is still a major problem and many related problems remain unresolved. Super function based machine translation was proposed to perform translation without going through syntactic and semantic analysis as many machine translation systems usually do. In such way, the import of the errors caused by the syntactic and semantic analysis could be avoided and the readability of the translation is also guaranteed. We continue to extend the super function for the Chinese Japanese machine translation. The super function is divided into the super function for sentences and super function for phrases. The existing scope and definition of the variable for super function is also expanded. The base noun phrases and base verbal phrases are detected and considered when building the super function. The head noun in base noun phrase is treated as variable but the determinative modifiers in base noun phrases are treated as modificatory variables, meanwhile the determinative modifiers in base verb phrases are treated as modificatory variables to build the super function. The revised Levenshtein distance algorithm is proposed for evaluate and find the matching super functions in database for input sentences and the determinative modifiers. The proposed extended super function keeps the advantages of the super function but has less number of super functions for Chinese Japanese machine translation. From the experiment results, the extended super function is not only effective but also feasible and with considerable precision for Chinese Japanese machine translation. The extended super function can even translate long sentences with complex structure for which the completely matching super functions can not be found for translation.


Construction of English Composition Support System Based on Conveying Emotion

With the increased demand for English communication, various styles of learning support methods have been proposed and provided to the Japanese learners. However, there are still many learners finding it hard to read, write and speak in English. Regardless of language difference, understanding the other's intention and emotional status accurately and expressing what they think or feel to the others appropriately are the important abilities for actual communication. This paper focuses on the linguistic performance ability especially to communicate emotion and introduce our prototype system that help describe the emotion in English.


Estimate Word Emotions Based on Part of Speech and Positional Information

The 5th International Conference on Intelligent Computing


Influence on Emotional Impression of Voice by Changing Prosodic Features

In this paper, investigate the relation between emotions represented by speech and features included in speech. There are two kinds of approaches in comparing experiment. Firstly, speech data are non-verbal such as white-noise, sine wave and vowel /a:/, so that investigate the relation between emotions and prosodic features only. Secondly, speech data include verbal information such as `ohayou'(hello), `usotsuki'(Ananias), so that investigate case speech data are words. Result of comparing shows the effect that of verbal information on emotions is strong, in case of words used this experiment. On the other hand, emotions felt by subjects are changed by variation of prosodic features in some words.


Detect the Possible Spokesperson with an Omni-directional Camera, in a Robot-human Communication System

When the man-machine communication system faces many users at the same time, it becomes a question that how to discover the spokesperson. In the complex environment, the noise may cause some difficulties for the robot to recognize the sound commands. To solve this question, we try to use the camera on the robot to find out the spokesperson. In this research, we propose a new image processing method that using omni-directional camera. The robot may find out all the possible s spokesperson around it, by detecting the human body and its movement.


Accurate Learning for Chinese Function Tags from Minimal Features

Proceedings of the ACL-IJCNLP 2009 Student Research Workshop

Association for Computational Linguistics

Data-driven function tag assignment has been studied for English using Penn Treebank data. In this paper, we address the question of whether such method can be applied to other languages and Treebank resources. In addition to simply extend previous method from English to Chinese, we also proposed an effective way to recognize function tags directly from lexical information, which is easily scalable for languages that lack sufficient parsing resources or have inherent linguistic challenges for parsing. We investigated a supervised sequence learning method to automatically recognize function tags, which achieves an F-score of 0.938 on gold-standard POS (Part-of-Speech) tagged Chinese text a statistically significant improvement over existing Chinese function label assignment systems. Results show that a small number of linguistically motivated lexical features are sufficient to achieve comparable performance to systems using sophisticated parse trees.


Construction of a Blog Emotion Corpus for Chinese Emotional Expression Analysis

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

There is plenty of evidence that emotion analysis has many valuable applications. In this study a blog emotion corpus is constructed for Chinese emotional expression analysis. This corpus contains manual annotation of eight emotional categories (expect, joy, love, surprise, anxiety, sorrow, angry and hate), emotion intensity, emotion holder/target, emotional word/phrase, degree word, negative word, conjunction, rhetoric, punctuation and other linguistic expressions that indicate emotion. Annotation agreement analyses for emotion classes and emotional words and phrases are described. Then, using this corpus, we explore emotion expressions in Chinese and present the analyses on them.


A Practical System of Domain Ontology Learning Using the Web for Chinese

The Fourth International Conference on Internet and Web Applications and Services

This paper proposes an ontology learning system model based on the Web search engine and Protege-OWL API, which emphasizes iterative learning approach by the extracted instances. We discuss taxonomic and non-taxonomic relationship learning separately in ontology learning system, and investigate the importance of verb plus noun phrase learning for extraction of activity concepts in Chinese. We also propose an algorithm of relevance measurement for extracting relation instances by binary keywords based on co-occurrence statistics. Finally, we build a practical system of ontology learning through learning relation instances of the Chinese festival ontology, and test the effectiveness of our method.


A Corpus-based Multi-label Emotion Classification using Maximum Entropy

Proceedings of International Workshop on Natural Language Processing and Cognitive Science

Thanks to the Internet, blog posts online have emerged as a new grassroots medium, which create a huge resource of text-based emotion. Comparing to other ideal experimental settings, what we obtained by modeling blogs would be more private, honest and polemic because web logs from the World Wide Web evolve and respond more to real-world events. In this paper, our corpus consists of a collection of blog posts, which annotated as multi-label to make the classification of emotion more precise than the single-label set of basic emotions. Employing a maximum entropy classifier, our results show that the emotions can be clearly separated by the proposed method. Additionally, we show that the micro-average F1-score of multi-label detection increase when the mount of available training data further increases.


the 7th Mexican International Conference on Artificial Intelligence (MICAI 2008)

In this paper, we propose a practical question answering system for the "Analects of Confucius" based on the pragmatics information. The "Analects of Confucius" is a classic Chinese literature by the Confucian during the Warring States Period (476 B.C-221 B.C ). But the literature can not be categorized by topics and it has comprehensive meanings, not only the literal meanings and explicitly communicated meanings of the passages, but also deeper meanings and implicitly communicated meanings (Here, we call the deep meaning and implicitly communicated meaning as pragmatics information). Therefore we constructed a QA system, which aims to help understanding the ''Analects of Confucius'' correctly. According to the experiments, the pragmatics information based retrieval results are more accurate than the results based on the interpretations in modern language.


Speaker Detection Method for Autonomous Robot In Complex Communication Environment Based on Image Processing

In order to provide a better man-machine communicationsystem, we hope the computer or the robot can understandand simulate humans' command. To solve this problem, weneed to enable the robot to recognize the speaker. However,in the realistic scene, the factors of the complexenvironment, such as the noise, may course somedifficulties to recognize the commands. So imitating theperformance of human, we try to use the eyes of the robotto solve the problem. In this research, we propose a newspeaker detection method based on image processing. Therobot might find and identified all the possible speakersaround it, by detecting the recognizing the peoples' faces. Itmight also find out the speaker who is speaking, by judgingwhether the possible speakers' mouth are acting. Using eyecameras and a omni-directional camera, we developed asystem and some experiments were conducted to verify thisnew system.


Question Classification in Chinese Restrictd-domain Based on SVM and Dictionay

Question classification is very important for question answering. This paper presents our research work on automatic question classification through support vector machine approaches. Unlike the classification using only bag-of-word features, we exploit the domain knowledge and question-specific stop words in our model, and also present how to enrich bag-of-word approach by implementing feature attributes to facilitate the question categorization. When tested on the questions in cooking domain, our approach reaches an accuracy up to 86.34%, which promisingly outperforms the result of the baseline.


Suicide of college students has been a universal phenomenon in the world. And the phenomenon has become more and more sever because of the complex and drastic competitions. With the popularization of Internet and the development of information processing technologies, a lot of people have established their own blog Websites to write down their experiences and express their feelings at times. It will be very helpful if the computer can recognize the emotions expressed in blog pages automatically. And then it will be convenient for teachers or psychological consultants to monitor the affective information of college students and take measures for the depression prevention when necessary. Owing to the advances in affective computing and natural language processing, researches have begun to pay more attention to the emotion recognition in NLP all over the world. This paper outlines the approach we have developed to construct a blog emotion-recognizing system. It is based on the lexical contents of words and structural characteristics of blog articles. For the emotion computing of articles, two methods are proposed and the experimental results are compared and analyzed. Finally, the implications of the results are discussed for the future's direction of the research.


Advanced Emotion Categorization and Tagging

Affective computing is attracting attentions as a popular growing field with many applications such as Kansei engineering, information retrieval and HCI. But until now the study of fine-grained theory of emotion is still a challenge. In this paper, a novel method to analyze emotion category is proposed according to the statistics of affective property in Dictionary of contemporary Chinese. These emotion categories are called complex emotion. Firstly, over 3,700 common affective words and their detailed explanations had been collected for an affective lexicon, then we analyze the consistent relationship in the affective lexicon and consequently 52 salient complex emotion states are categorized and tagged by a straightforward clustering algorithm. The complex emotions are compared to the traditional definitions of basic emotions in psychology and have been evaluated to be valid in the experiment. Moreover we also have tagged the semantic orientation for the collected words.


Sentiment and Sentimental Agent Identification Based on Sentimental Sentence Dictionary

This paper presents a method of sentiment and sentimental agent identification based on Chinese sentimental sentence dictionary. Our method can identify eight kinds of sentiment (including joy, sorrow, love, disgust, surprise, anxiety, anger and hate), and the main sentimental agent. Sentimental sentence dictionary is composed by some sentimental sentence patterns. And the sentiment of a candidate sentence is identified by calculating the consistency with the sentimental sentence patterns. Especially, using the sentimental sentence dictionary, we can get rid of the sentences without sentiment, but have sentimental words. According to sentiment words and expressions, the sentences which are not appeared in the sentimental sentence dictionary also can be identified. The experiments show that we can get precision of 84% for sentiment identification, and precision of 69% for sentimental agent identification.


Sentence similarity computing plays an important role in the question answering (QA) system. Because there are many question expressions for one meaning, we present a new approach to match question based on fuzzy set. In this paper, we establish a library of standard questions. Each standard question is relative with a series of Keywords. The main focus of this paper lies with matching of standard questions and questions asked by users. An experimental system based on the proposed method has been built, and the results of our experiments shows the proposed method is effective for question matching.


Emotion Recognition Based on Negative Words and Pattern Matching for Chinese Negative Sentence


Negative Expression Translation in Japanese and Chinese Machine Translation

Because there are many differences between Chinese and Japanese about the nature, history, culture, life, manners and customs, it is natural that there are particular words in each language. It is necessary that these particular words meaning are mastered right and translated accurately in translation. Because of the complex corresponding relationship with Chinese in Chinese-Japanese machine translation, it is easy to occur a vagueness, due to the negative in the sentence, many mistranslations are caused by the commercial translation software. In this paper, we analyzed the negative expressionways in Chinese and Japanese languages to translate Chinese negative sentences by using the selection rules of Chinese negative words and position rules. If we translate the basic Japanese negative expression naide1 into Chinese, its meaning is mei2, bie. Translate nakute into Chinese, according to its negative meaning and grammatical rule, it may be translated into bu. In the current research, we investigated a rough use rules which abstracted from each typical examples including naide and nakute.


Exploiting Lexical Information for Function Tag Labeling


Dual-chain Unequal-state CRF for Chinese New Word Detection and POS Tagging

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


Automatic Construction of Biomedical Abbreviations Dictionary from Text

The size and growth rate of biomedical abbreviation are increasing very fast, automatic construction of biomedical abbreviations dictionary from text helps to understand biomedical literature, and to update existing databases, ontologies, and dictionaries. This paper proposes a new method for automatic construction of biomedical abbreviations dictionary from text by combining string matching algorithm and searching algorithm. The string matching algorithm extracts abbreviations and their longforms. The searching algorithm corrects the false longforms produced by the string matching algorithm. The searching algorithm is based on the idea that readers often lookup relative articles to judge the longform of an abbreviation is correct or not. Our experiments show that the algorithm has high precision (97.5%) and recall (82.2%). And because tagged corpus is not necessary, the method has high efficiency.


Probabilistic Neural Network Based Text Summarization

This work proposes an approach to address the problem of improving content selection in automatic text summarization by using probabilistic neural network (PNN). This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity, sentence inclusion of numerical data, sentence relative length, Bushy path of the sentence and aggregated similarity for each sentence to generate summaries. First we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train the probabilistic neural network (PNN) in order to construct a text summarizer model.


Generating Lexico Pattern for Dimensioning of Chinese Separable Verb-object Words in Chinese Text

In this paper, we pay attention to the Chinese separable verb-object words in sentences. We collect sentences with morphemes of separable words employing search engine for thousands of separable words collected by linguist. Then sentences are tagged artificially with agreement of whether being used separable words on the result of word segmentation and part-of-speech tagging, According to typical grammar patterns discovered by linguists and patterns shared among collected sentences, lexical patterns are generated and optimized for further process of separable word tagging in Chinese document after word segmentation and part-of-speech tagging.


STATISTICAL MODEL BASED TEXT SUMMARIZATION

This work investigates different text features to select thebest one and proposes an approach to address automatic textsummarization. This approach is a trainable summarizer,which takes into account several features, including sentenceposition, sentence centrality, sentence resemblance to the title,sentence inclusion of name entity, sentence inclusion ofnumerical data and sentence relative length for each sentenceto generate summaries. First we investigate the effect of eachsentence feature on the summarization task. Then we use allfeatures score function to train feed forward neural network(FFNN) in order to construct a text summarizer for this model.The proposed approach performance is measured at severalcompression rates on a data corpus composed of 50 Englisharticles from the domain of politics.


A Simple Method Improving Precision of Cross Domain Bilin-gual Sentence Alignment

In this paper, we present three models distinguished by combining different language features, for the task of cross domain Chinese-English sentence alignment. The separat-ing capability of each single and combination of these fea-tures are compared, with further discussion of influences on performance. For each feature we introduce a polarity pa-rameter to measure the distinction between aligned pairs and other candidate pairs. Finally, thresholds are set to po-larities to further enhance performance precision (from 98.00% to99.69%).


AN ERP RESEARCH ON CHINESE JAPANESE LEARNERS' PROCESSING OF JAPANESE KANJI AND SENTENCES

This paper aims to investigate the recognition process ofJapanese Kanji and sentences for Chinese Japanese learners(CJL) by analyzing the event-related potential (ERP)differences between CJL and JNS (Japanese nativespeakers) while they visually recognized Japanese Kanjiand sentences. The experiment results showed that nosignificant differences were found between the two groupsin the Japanese Kanji condition, but the differences werefound in the Japanese sentences conditions. The N400latency for JNS appeared earlier, and the amplitude wasbigger than CJL, but the duration for CJL was longer thanthat for JNS. These results demonstrated that the difficultydegree for CJL was bigger than that for JNS on thesemantic processing of Japanese sentences. The P600appeared broadly following the N400 component for JNS,in contrast, the P600 only appeared over the right frontallobe for CJL. This demonstrated that the neuralmechanisms of recognition process of Japanese sentencesbetween the two groups were not identical. JNS alsoperformed syntactic processing after completed semanticprocessing. From the results, for CJL, the difficulty ofJapanese language learning was the recognition andunderstanding of Japanese sentences which includinghiragara, not Japanese Kanji.


Exploring Words with Semantic Correlations from Chinese Wikipedia

In this paper, we work on semantic correlation between Chinese words based on Wikipedia documents. A corpus with about 50,000 structured documents is generated from Wikipedia pages. Then considering of hyper-links, text overlaps and word frequency, about 300,000 word pairs with semantic correlations are explored from these documents. We roughly measure the degree of semantic correlations and find groups with tight semantic correlations by self clustering.


A METHOD OF EMOTION JUDGEMENT IN EMPATHETIC UNDERSTANDING

Purpose of this study is to judge human emotions frombrain activity of physiological phenomenon. Especially,we focus to empathetic understanding which is psychologicaltechnique. In this paper, we propose a empathyjudgement technique using subjective evaluation usingquestionnaire and objective evaluation using brainwaves measured by EEG. In results of experiment, itturns out that to judge empathy is difficult in the subjectiveevaluation using questionnaire and to judge empathyis possible in the objective evaluation using brainwaves. In addition, it turns out that emotions judgedfrom questionnaire and from brain waves are different.Thus, empathy judgement using both results generatea synergistic effect.


ANALECTS OF CONFUCIUS SELF-LEARNING SYSTEM USING PRAGMATICS AND TOPIC

The purpose of this study is to construct a new selflearningsystem that supports the Analects of Confuciuslearning for the entry-level learner. We presentpragmatics and topic information to the learner. Thereare two roles of pragmatics and topic information. Thefirst one is to clarify the verse semantic and the secondis to make Analects of Confucius contents familiar tothe learner. The experiments evidenced the effectivenessof the presented pragmatics and topic informationthat is specific to increase enthusiasm for learning andenhance understanding of the contents.


EMOTION INFERENCE METHOD BASED ON WORD'S MEANING AND UTTERANCE FEATURES

Emotion recognition is necessary to realize smooth communicationbetween human and computer. This studyproposes an emotion prediction method based on twocommunication measures: words and utterances. Concretely,we investigate the emotions caused by meaningfulexpressions such as arigato (Thank you.) andbaka (Stupid.). Features of utterance such as utterancespeed, maximum value of power and the highestbasic frequency are extracted from four speeches. In thecase of male utterance, we learned that invoking emotionsof the listener was defined by the only utterancespeed regaredless of words meaning. But, in the caseof female utterance, we learned that invoking emotionsof the listener was defined by the utterance speed andmaximum of power regaredless of words meaning.


A ANALYSIS OF EMPATHETIC UNDERSTANDING USING RELATION BETWEEN ELECTRODE AND FREQUENCY

In this research, we propose technique to judge havingempathic understanding or not which compared subjectiveemotions of the subjects to estimate from questionnaireand objective emotions of the subject to estimatefrom brain waves measured by EEG. Specifically, subjectswatched trailer of the movies and assume baselinesthat watching trailer and compare the control groupthat did not give instructions with the experimentalgroup whom we directed to empathic understanding.


COMPARISON BETWEEN THE HUMAN EMOTION TRANSFER RATIO AND THE SIMILARITIES OF EMOTION

Existing methods to recognize emotions of a sentence canrecognize a few kinds of emotion, and many methods use adictionary that emotion weights is related every words. Ouraims are to propose a method which can recognize with highprecision using feeling expression and can add new recognizableemotion easily. To realize the proposed method, weproposed an emotion similarity calculation. This method calculatessimilarity between emotions that two sentences represented.In the emotion similarity calculation, emotion similaritybetween an input sentence and classified sentence byemotions is used. The classified sentences are in emotioncorpora. The calculation formula is based on BLEU whichis a machine translation evaluation method. In this paper,an emotion similarity calculation method using N-gram frequencydictionaries is shown. This method can recognizewith high precision compared to the past one. The N-gramfrequency dictionaries are made from each emotion corpora.To examine the recognition accuracy with our method, wecompared the precision and the human emotion transfer ratiobetween humans. As a result, we found the precision is89.91% of the ratio. This result means the recognition withour method can recognize like a human.


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

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


Support Vector Machine based Text Summarization

This work investigates different text features toselect the best one and proposes an approach to addressautomatic text summarization. This approach is a trainablesummarizer, which takes into account several features,including sentence position, sentence centrality, sentenceresemblance to the title, sentence inclusion of name entity,sentence inclusion of numerical data, sentence relative length,Bushy path of the sentence and aggregated similarity foreach sentence to generate summaries. First we investigate theeffect of each sentence feature on the summarization task.Then we use all features score function to train SupportVector Machine (SVM) in order to construct a textsummarizer model. The proposed approach performance ismeasured at several compression rates on a data corpuscomposed of 100 English articles from the domain of politics.


A Simple Method Improving Precision of Cross Domain Bilingual Sentence Alignment

International Conference on Advanced Intelligence (ICAI-08)


Exploiting Syntactic and Semantic Information in Coarse Chinese Question Classification

The 4th IEEE International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE)


An Approach for Evaluating Emotion Tagged Corpus

IADIS International Conference Interfaces and Human Computer Interaction 2008

This paper proposes a method for evaluating our emotion corpus and describes the result of evaluation. The emotion corpus was originally created in Japanese and English to be used for emotion recognition research and consisted of dialogue sentences including emotional expressions. To ensure the reliability and use the corpus as the standard of emotion evaluation, the objective evaluation is necessary because single person annotated tags on the corpus. For Japanese part of corpus evaluation, we conducted a questionnaire to the several examinees. The examinees were asked to tag arbitrarily emotion categories on the sentences in the corpus. The obtained sentence emotion tags were classified then the match rate of the initial tag and the tags annotated by the examinees. Reliability of each emotion tag annotated on the sentence was calculated and added as emotional weight on each sentence emotion tag. The 10-fold cross validation test was conducted using the weight added corpus to estimate sentence emotion and obtained recall of 0.62 on average.


A Study of speaker identification using phoneme-information


An Algorithm for Extracting Emotion Occurrence Sentence Pattern from Corpus

In this paper, we propose a method of extracting sentence patterns for estimating speaker's emotion.Our method estimates speaker's emotion by using a dictionary that registered the sentence pattern extracted from sentences expressing speaker's emotion.Because sentence types expressing emotion are various, we focused on dialogue sentence that includes important elements such as subject or object and proposed a method to extract the sentence pattern automatically.This paper describes the accuracy for converting the sentence including emotional expressions into the sentence patterns.We conduct experiments for emotion estimation based on the sentence pattern then describe the result of the evaluation.


PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY

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


6th WSEAS International Conference on CIRCUITS, SYSTEMS, ELECTRONICS,CONTROL & SIGNAL PROCESSING


Framework of Affective Information Processing for Next Generation Communications

Proceedings of 2007 National Conference on Artificial Intelligence

The extraction and recognition task of affective information are attracting researchers in order to realize emotion communications between machine and human beings. Human psychological characteristics are focused on to develop general-purpose agents that can complete the task of human emotion recognition and machine emotion generation. In this case, we comprehensively analyze the features such as brain waves, voice sounds and images that represent information included in emotion elements of phonation, facial expressions, and speech usage. We analyze and estimate many statistical data based on the latest achievements of brain science and psychology in order to derive transition networks for human psychological states. A multimedia and multifunction project for emotion recognition, estimation and representation is improving through establishing a speaker word model for researching psychological change and emotional presentation, proposing a new approach for recognizing human emotion based on Mental State Transition Network, and developing one emotion estimation method based on sentence pattern of emotion occurrence events.


Orient video database for facial expression analysis

Proceeding of the 10th IASTED International Conference Intelligent Systems and Control

The research on affective computing and related applications are attracting attentions during the last decades, particularly in Japan with the development of e-business. In this case, the human facial expression data is necessary to studies of emotion recognition and synthesis. There are many famous existed facial expression databases but they are primarily composed by Europeans or American subjects. The cultural background differences generate the gap between their styles of emotion expression. For an instance, comparing to the Europeans, the orient like Japanese do not have obvious facial expression. In this paper we propose a human facial expression video database named as AONE (Asian Oriental Expression) for orient in order to analyze and recognize the emotion automatically through human facial expression. We established the database of 75 adults mainly from orient, which included more 2000 samples of video sequences of facial expression from both frontal and side view. By consideration of the existed famous facial expression databases such as KANADE database form CMU, we designed the database containing both the spontaneous and deliberate facial expressions.


Comparison of corpus-based emotion estimation methods

Internationalization of information is increasing needs for a computer to deal with multilingual communication. Understanding not only semantic information but also underlying emotion in textual information can contribute to assist such communication and also to make human-computer interaction more natural and congenial. For the purpose of emotion estimation from multilingual text, we created bilingual emotion annotated corpus in Japanese and English. Based on the corpus, we statistically analyzed the characteristics of how word emotion affects sentence emotion in each language. Two kinds of emotion estimation methods were proposed by using the existing techniques considering the feature quantity obtained from the corpus statistics then evaluation experiments were carried out.


The differences in Japanese language processing between Japanese and Chinese:Evidence from event-related potential


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

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 apparatus 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 noises: 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.


An Affective Virtual Character Based on the Mental State Transition Network


Designing of Campus Information Navigator based on Human-Machine Dialog


Nolinear Luminance-Chrominance Spase Map based Speak Detection by Hidden Markov Model


Super-Function Matching of Causative Sentences for Chinese-Japanese Machine Translation


Construction and Analysis of an Chinese Emotion Corpus

This paper presents an effective and practical approach on the emotional lexicographic component of Chinese documentation. Most previous works have focused on identifying emotion according to affective computing. In this paper, we will focus on constructing and analyzing one part of our emotion thesaurussentence corpus. Additionally, an automatic sentence gathering tool and a sentence corpus are developed.


A Hybrid Approach to Determiniing Semantic Dependency Relations of Chinese Sentences


Extraction of the term which has an explicit relation using Coincidence Frequency


An Emotion Similarity Calculation Using N-gram Frequency


The New Image Processing Method in Expression Recognition System


Similarity Computing Based on VSM and Syntactic Analysis


Automatic Utterance Segmentation Tool for Speech Corpus

We collect the speech data for investigating an intra-speakers' speech variability over a short and long time. In general, to reduce the load of speakers, the speech data are collected as one file from collecting start to collecting end. Hence, there are some noises, non-speech sections and mistaken sections in this file. Consequently, we must segment this file into individual utterances and select the useful utterances. This process requires a lot of time and efforts. In this paper, we propose an automatic utterance segmentation tool for dividing the collected speech data. The proposed tool is composed of four processes, which are a voice activity detection, speech recognition, a DP matching, and a correct of speech section. For evaluating the proposed tool, we conduct the evaluation experiments using a female speaker's speech data in our corpus. Experimental results show that the proposed method can reduce a filing time by 90% compared to a manual filing. In This paper, first, we introduced the large speech corpus. This speech corpus contains is the speech data collected by specific speaker over long and short time periods. And, we explained the automatic utterance segmentation tool which we made in the case of corpus build. And inspected the validity. As a result, it was demonstrated that the automatic utterance segmentation tool was high-performance. Furthermore, it was demonstrated that speech corpus build became simple by using the automatic utterance segmentation tool.


Analysis of Variation on Intra-Speakers Speech Recognition Performances

Even if a speaker uses a speaker-dependent speech recognition system, speech recognition performance varies. However, the relationships between intra-speaker's speech variability and speech recognition performance are not clear. To investigate these relationships, we have been collecting speech data since November 2002. In this paper, we analyze the relationships between intra-speaker's speech variability and the phoneme accuracy by a correlation analysis. Analyzed results showed the strong negative correlation between the phoneme accuracy and the speaking rate. The correlation coefficient indicated -0.77. Moreover, we can see that the phoneme accuracy is correlated with the temperature in the recording room and the humidity difference.


Outcome Management and Morphologic Variance Analysis Using PsychomsTM for Patient Care in Psychiatric Hospitals

In 2003, the Japanese government set an approximate goal of 72,000 social hospitalization in-patients being discharged in 10 years from psychiatric hospitals to the community. To meet this challenge, the clinical pathway (CP) is a useful model for managing the team's progress in providing health care services. High quality discharge planning for patients requires continuous refinement of CP including the collection and analysis of variance data, i.e., information not listed in the CP model. The purpose of this study is to analyze variance data gathered using PsychomsTM to find unidentified issues with keyword sorting (keyword variance analyses system) and text-mining (text mining variance analyses system). Using morphological analysis to detect word occurrences in variance data, it may be possible to identify problems needing solution by developing automatic rankings to analysis outcomes. To achieve this, however, specialized experience is required for variance analysis. Also, there must be high congruence between the results of text mining variance analyses and keyword variance analyses systems.


Emotion Voice Analysis System Connected to the Human Brain

To investigate human brain activities in association with emotional speech, we developed a novel voice analysis system connected to a functional magnetic resonance imaging (fMRI) machine. Participants spoke inside the MR magnet during which BOLD activities of the brain was measured. Speech voice was transmitted through the newly developed mask-microphone inside the magnet to the external computer and was processed by the emotional voice analysis system. Two participants conversed without hindrance and their emotional state was analyzed. Using the system, we were able to detect brain activities during speech and simultaneously evaluate the human emotional voice.


Emotion Recognition from Text based on the Rough Set Theory and the Support Vector Machines

In recent years, several methods on human emotion recognition have been published. But computer application on Chinese natural language processing (NLP) is still on the starting stage. In this paper, we proposed a scheme that emotion recognition from text through classification with the rough set theory and the support vector machines (SVMs). The basic steps are firstly to sample data sets, to build the decisions table, and to find importance of attributions and the simplest form of decisions table according to relative reduction and then the rough set model of system is obtained, finally train the predicting model by the SVMs. Our experiment results show that rough set theory and SVMs method are effective in emotion recognition, and the high recognition rate is resulted.


A Method of Semantic Dictionary Construction from On-line Encyclopedia Classifications

This paper introduces a method of constructing a semantic dictionary automatically from the keywords and classify relations of the web encyclopedia Chinese WikiPedia. Semantic units, which are affixes (core/modifier) shared between many phrased-keywords, are selected using statistic method and string affix matching, also with other units to explain the semantic meanings. Then the result are used to mark the semantic explanations for most WikiPedia keywords by analyzing surface text or upper classes. The feature form ' structure or advantages comparing to other semantic resource are also concerned.


Questions Analysis of Confucius Analects Knowledge System

In this paper we explore the method based on keywords to analyze the questions for Confucius Analects Knowledge System. After analyzing the questions we can find the answers from the knowledge database of Confucius Analects. Confucius Analects Knowledge System is regard as a specific domain Question-Answer system. Taking a reference to the chapters' structure of Confucius Analects and the literature on Confucius Analects the chapters are classified into 8 categories. One of questions analysis' work is deciding the categories of questions. We use How Net to get all meanings of keywords then select congruent meanings for each category. Questions' categories depend on the keywords. All the questions collected are clustered by k-medoids algorithm. We find some rules from the clusters so that all the questions are classified into several types. The other work of questions analysis' is deciding the questions' types. Then we can find answers from the knowledge base.


Automatic Super-Function Extraction for Translation of Spoken Dialogue

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


Chinese complex long sentences processing method for Chinese-Japanese machine translation

The research on machine translation has been lasting for many years, and now this research field is increasingly a thoroughly refined. In practical machine translation system, the processing of a simple and short Chinese sentence has somewhat good results. However, but the complex long Chinese sentence translation still has difficulties. In this paper a new hierarchical approach processing for Chinese complex long sentence through analysis of Chinese punctuation, conjunctive words and syntax function is proposed. The method synthetically uses semantic characteristic of source Chinese sentence, which includes grammatical features, the length of the source sentence, punctuation and functional words. First phase is conjunctive words for simplified segmentation, and then the syntax analysis, in order to process complex long sentence by the multi-hierarchical approach. A long sentence is divided into several parts; every part gets the correct translation of the result separately, and then is combined by the comprehensive approach to gain the complex long sentence translation result. Experiments show that our approach can significantly reduce the time consumption and numbers of ambiguity, and also improve the accuracy and readability when parsing Chinese complex long sentence.


Ontology based Domain Knowledge Construction

This paper presents a representation framework for domain knowledge based on the analysis of Chinese festival by ontology analysis approach; it also discusses the organization method for describing domain data based on ontology. At the same time, the paper summarizes the ontology study domain. It is conceptualized using method of domain knowledge by the framework, and that is to say, to construct the concept, the taxonomy of concept and the concept properties for the domain knowledge is based on ontology.


READING: A Self Sufficient Internet News System with Applications in Information and Knowledge Mining

This paper presents the READING (retrieving news around the world for discovery and knowledge mining) system, which is a self sufficient system for aggregating and classifying news from around the world with a focus on Asia. The system, however, goes beyond the traditional news aggregation sites by mining information and knowledge from the news articles to aid in such tasks as decision making. The system is able to collect, classify and mine knowledge automatically without the need for an editor or intervention by an administrator. Average users, news professionals and researchers alike will find that the system offers something for them. This paper focuses on the underlying algorithms in the system and how they interact in creating the overall system.


Question Answering System of Confucian Analects

This paper constructs a question answering system of Confucian Analects. As a result of context change and the difference of words' connotation between modern Chinese and ancient Chinese, the accuracy of content-based retrieval and category-based retrieval in the classical literature is quite low. In view of this, the paper has established the categories and pragmatics information base for Confucian Analects. It also proposes a retrieval method based on pragmatics information and categories. To increase accuracy and efficiency, the category keyword collection and the question type keyword table are established as well. When the system recognize the type and category of the user's question, it uses key word semantic matching. Namely, the category keyword collection and the question type keyword table are separately used to decide the category and the type. The experiments evidenced the effectiveness of answer extraction approach based on pragmatics information specific to in query with deep meaning.


Corpus Based Analysis of Japanese-English Emotional Expressions

Development of information technology has increased the chances of interaction between human and computer. Understanding not only semantic information but also underlying emotion will make communication with computer more natural and congenial. As preliminary research for emotion estimation from multilingual text, we focused on emotion expressions of Japanese and English and created a corpus with about 1,200 bilingual sentences and tagged emotions on words and sentences. Based on the corpus, we statistically analyzed the characteristics of how emotions were expressed in each language and also the relationships between sentence emotion and word emotion in the sentence. The results show the characteristics of emotion expressions in Japanese and English dialogue sentences.


Electroencephalograph Estimation Method of Measuring `Empathic Understanding'

It is important for health care staff to understand patients' emotion and affective changes. Health care staffs should understand the patients' world as if this was his her own. This is called "empathic understanding". To understand the emotional process, electroencephalograph recording (EEG) was performed on five healthy participants while watching the sentimental movie which was 24-minutes long. The EEG changes during the emotional scenes were collected and compared with emotionally stable states. By using power map and spectrogram analyses of EEG, alpha bands at the occipital region were seen specifically among the emotionally impressed states. It seemed that the changes indicated the objective expression of empathic understanding. We suggest that EEG with power map and spectrogram analyses is a useful fundamental tool for developing a second generation computer system adjusted to the changes of one's mind. However, further examinations to remove artifacts among the EEG results are needed.


Classification of Facemarks Using N-gram

In this paper, 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 communication. 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 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 facemarks classification using N-gram and evaluate this method.


Extracting the Opinions of News Articles based on Emotionally Laden Words

In this paper we propose an approach to extract the opinion of the media from news articles based on emotionally laden words. In the propose approach, the opinions are judged by the total number of the emotion levels from articles. The emotion level is the number from -3 to +3 that indicate emotionally laden words whether have plus or minus images. The articles of baseball game and the editorial article are used as experiment object. The precision of the propose approach on the articles about baseball game is 66%, and on the articles of political event is 36%.


A Semantic Information Retrieval Technique and an Evaluation for a Narrow Display Based on an Association Mechanism

The purpose of this study is to output information which users hope as high ranking results on narrow display of mobile information terminals. The concept of the proposed algorithm is to understand the meanings and the contents expressed by keywords and to retrieve appropriately related information. Concretely, we propose an information retrieval technique to evaluate the relationships between words using an association mechanism. A new test collection was made by 100 examinees who judged the retrieval results according to text meanings. Retrieval performance was objectively confirmed with the experiment based on the test collection.


Artificial Bandwidth Extension for Speech Signals using Speech Recogniton

International Speech Communication Association


Automatic Text Summarization Based on Lexical Chains and Structural Features

Proceedings of 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

The rapid growth of the Internet has resulted in enormous amounts of information that has become more difficult to access efficiently. The primary goal of this research is to create an efficient tool that is able to summarize large documents automatically. We propose concept chains to link semantically-related concepts based on Hownet knowledge database to improve the performance of Text Summarization and suit Chinese text. Lexical chains is a technique for identifying semantically-related terms in text. The resulting concept chains are then used to identify candidate sentences useful for extraction. Moreover, the other method based on structural features which can makes the summary of the text have more general content and more balance is also proposed. The final experimental results proved the effectiveness of our methods.


Semi-Automatic Construction of an Emotion Ontology Using HowNet

Proceedings of the 2007 International Conference on Artificial Intelligence and Pattern Recognition

Emotion ontology construction is a desirable task in affective computing. In this paper, we propose to semi-automatically construct an emotion ontology using HowNet. First, an affective event hierarchy is extracted from HowNet's semantic event dictionary, which contains the semantic roles associated for each of the events. Next, three different types of tags are assigned to the semantic roles, of the predicates in the affective predicate hierarchy, creating an emotion predicate hierarchy. Then, a 120,496-words-scale knowledge dictionary is used to transform the emotion predicate hierarchy into an emotion ontology. In the end an ontology containing just under 5,500 verb concepts was created.


Low Cost Japanese-English Machine Translation for Cross-Lingual Information Retrieval

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


Study of Relationships Between Intra-Speaker's Speech Variability and Speech Recognition Performance

2006 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2006)

Even if a speaker uses a speaker-dependent speech recognition system, speech recognition performance varies. For this reason, speech quality is varied by some factors, including emotion, background noise, and so on, even though the speaker and utterance remain constant. However, the relationships between intra-speaker's speech variability and speech recognition performance are not clear. Hence, we focus on the intra-speaker's speech variability which affects the speech recognition performances. To investigate these relationships, we have been collecting speech data since November 2002. Using a part of the speech corpus, we conducted speech recognition experiments. In this paper, we analyze the relationships between intra-speaker's speech variability and the phoneme accuracy by using the correlation analysis. For factors of the correlation analysis, we use a number of errors, a speaking rate, a likelihood. Analysis results show a strong correlation between the number of the substitution errors and the phoneme accuracy although the correlations of the number of the deletion and the insertion errors are low. Therefore, it is considered that there are overlaps between phonemes since the feature parameters vary at each speaking rate. For improving the phoneme accuracy, it is needed that we study a method which discriminates phonemes. On the other hand, although the correlation between the phoneme accuracy and the speaking rate seems to be low, a strong correlation between the speaking rate and the number of deletion errors and insertion errors are found. Since the number of the insertion errors and the number of the deletion errors were in the counterbalance relation, the correlation between the speaking rate and the phoneme accuracy was low. However, we consider that it is needed to normalize the speaking rate because the speaking rate influences on the number of the deletion and the insertion errors.


Measurement of the "emotional sensitivity" and "emotion at moment" using Power Map Analysis and Emotion Spectrum Analysis Method

Proceedings of International Symposium on Artificial Intelligence and Affective Computing 2006

International Advanced Information Institute

The purpose of this study was to determine the emotional sensitivityof persons through empirical determination of electroencephalographic (EEG)data. With EEG electrodes in place, participants were requested to view anemotionally-charged video presentation.'Sensitivity at the moment' wasmeasured and found to be significantly changed, showing evidence ofemotional sensitivity at the point of occurrence. This change facilitates theunderstanding of caring emotion as openness to a formed intention of knowingthe other as caring person. Competencies in caring for persons are critical tounderstanding the valuing of technological competency as expression of caring.Technological competency as knowing others intentionally as 'sensitive beings'provided the impetus for the development of 'sensibility estimation technique'using brain wave activities to measure it.


Constraint-based Chinese-Japanese Bitext Alignment

Bitext alignment is useful to many Natural Language Processing taskssuch as machine translation, bilingual lexicography and word sense disambiguation.This paper presents a Chinese-Japanese bitext alignment. After describing somecharacteristics in Chinese-Japanese bilingual texts, we first investigate somestatistical properties of Chinese-Japanese bilingual corpus, including the correlationtest of text lengths between two languages and the distribution test of length ratiodata. We then pay more attention to n-m(n>1 or m>1) alignment modes which areprone to mismatch. We propose a cross constrain measure based on Hanzi charactersinformation for these kinds of alignment modes. By using dynamic programming,we combine statistical information and Hanzi character information to find theoverall least cost in aligning. Experiments show our algorithm can achieve goodalignment accuracy.


Classification of Facemarks Using Statistics of Characters