Extraction of keywords from disease-fighting blogs by fine-tuning the T5 model

2022 6th International Conference on Natural Language Processing and Information Retrieval

To measure lifestyle-related diseases such as heart disease and diabetes, which are on the increase, information on patients' lifestyles is necessary. With the growth of the Internet culture in recent years, a great deal of valuable information about patients' long fight against the disease, including lifestyle and condition changes, is now being written in the form of blogs and tweets. In this study, keywords related to changes in health status are extracted from "disease-fighting blogs" using Named Entity Recognition system, which is fine-tuned the "T5 Model", one of the latest deep learning and natural language processing models. This method can be applied to a system that informs patients and health care professionals how their lifestylehabits affect their health status.


Emotion and personality estimation of utterances in a dialogue model reflecting personality

Proceedings of 4th International Conference on Technological Competency as Caring in Nursing and Health Sciences 2022

One of the factors that enable humans to interact with AI in a friendlier manner is personality, and since the personality required of AI varies depending on the situation and context, a mechanism is needed that allows AI to switch between different personalities depending on the person with whom he or she is interacting. In this study, we use a Japanese language Transformer Encoder-decoder dialogue model (Japanese-dialog-transformers) and aim to construct a dialogue model that reflects the personality of the target person by fine-tuning the interview dialogue data. By analyzing the output of the proposed dialogue models using the MBTI estimation model and the sentiment analysis model, we evaluate the consistency of the personality and discuss the differences and similarities among the dialogue models.


Affective Analysis and Visualization from Posted Text, Replies, and Images for Analysis of Buzz Factors

Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VIII

In this study, we propose a method to visualize the factors that contribute to the buzz phenomenon triggered by Twitter posts. The analysis included tweets, images, and replies. Replies are after-the-fact responses posted in response to a posted tweet and therefore cannot be used to predict buzz phenomena. Therefore, they cannot be used to predict the buzz phenomena. In this study, the tweet body, images, and reply text were feature vectors, and an affective analysis model was constructed. Visualization of the relationship between the sensibility features output from this model and the number of RTs and likes (echo index), which represent the scale of the buzz, will be useful for analyzing the factors behind the popularity. Consequently, the subjective sensibility information with the most likes also tended to have a higher degree of similarity among the sensibility vectors.


A System to Correct Toxic Expression with BERT

Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD

This paper describes a system for converting posts with toxic expression on social media, such as those containing slander and libel, into less-toxic sentences. In recent years, the number of social media users as well as the cases of online flame wars has been increasing. Therefore, to prevent flaming, we first use a prediction model based on Bidirectional Encoder Representations from Transformers (BERT) to determine whether a sentence is likely to be flamed before it is posted. The highest classification accuracy recorded 82% with the Japanese Spoken Language Field Adaptive BERT Model (Japanese Spoken BERT model) as a pre-trained model. Then, for sentences that are judged to be toxic, we propose a system that uses BERTs masked word prediction to convert toxic expressions into safe expressions, thereby converting them into sentences with mitigated aggression. In addition, the BERTScore is used to quantify whether the meaning of the converted sentence has changed in meaning compared to the original sentence and evaluate whether the modified sentence is safe while preserving the meaning of the original sentence.


Music Video Search System Based on Comment Data and Lyrics

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery(Lecture Notes on Data Engineering and Communications Technologies; LNDECT)

Many people currently use video-sharing services such as YouTube. Keyword search is prevalent in these services. It can be hard to find a video that matches the users' interests using keyword search unless appropriate words are used. In this study, we propose a method for retrieving music videos with similar impressions by analyzing comment data from YouTube viewers and music lyrics. The proposed method converts comments and lyrics into vectors using Word2Vec, and music videos with similar impressions are retrieved using fuzzy c-means clustering According to the mean reciprocal rank (MRR) scores, it was clear that the output of the music videos had the same impression within the top three songs.


Analysis of Lifestyle Habits from Weblogs of Patients with Intractable Diseases Using Deep Learning

Proceedings of International Conference on Electrical, Computer and Energy Technologies (ICECET)

In recent years, patient-centered medicine has become widely accepted. To keep a record of their struggles,patients sometimes publish their diaries (disease blogs) on weblogs to be read by an unspecified number of people. In particular, writing a blog may be helpful in the case of intractable diseases of which there are few cases, and patients often feel isolated because they know few people with the same disease. In this study, we used deep learning technology to analyze the blogs written by patients with intractable diseasesand to determine how they live their daily lives and their feelings. If their lifestyles are understood, the relationship between the physical condition and mental changes of people fighting intractable diseases can be determined, and a system that shows behavioral guidelines for new users fighting similar intractable diseases can be built.


Risk Analysis of Developing Lifestyle-related Diseases based on the Content of Social Networking Service Posts

Innovation in Medicine and Healthcare, Part of the Smart Innovation, Systems and Technologies book series

Japans leading causes of death are cancer, cardiovascular disease, and diabetes, which account for approximately 60% of all deaths. All these diseases are closely related to lifestyle habits such as diet, exercise, and sleep and are called lifestyle-related diseases. Lifestyle-related diseases are characterized by a lack of subjective symptoms, making it difficult to notice the deterioration of the disease. In this study, we analyzed tweets related to diet, exercise, physical condition, and mental state from the contents posted by users on social networking services (SNS) to construct a system that can prevent lifestyle-related diseases more simply. We found that the results of this work can lead to the construction of a system than can detect risk factors leading to the development of lifestyle-related diseases, from the contents of users posts.


Topic Segmentation for Interview Dialogue System

Proceedings of 2021 5th International Conference on Natural Language Processing and Information Retrieval

In this study, topic segmentation was performed by referring to the interview dialogue corpus. Utterance intention tags were added to the existing interview dialogue corpus, and uttered sentences were vectorized using BERT, Sentence BERT, and Distil BERT. In addition, topic classification was performed using the utterance intention tags and the features of the preceding and following uttered sentences. Consequently, the greatest accuracy was achieved when the utterance intention tag was used with DistilBERT


Prediction of Number of Likes and Retweets based on the Features of Tweet Text and Images

The current study aimed to investigate social media trends and propose an analysis method to explore the factors underpinning the buzz phenomenon on Twitter. As it is not always possible to determine the cause of the buzz phenomenon from the text content alone posted on Twitter, we limited the analysis to tweets with attached images and devised an analysis method using both text and images. We investigated whether there is a relationshipbetween the features of both tweet text and its attached images, and how the relationship between these features is related to the number of likes and retweets (RTs) receivedthat is, indicators of popularity. We trained a multi-task neural network that takes the features extracted from the images and text as input, and then outputs the number of likes and RTs before extracting the feature vectors of the same dimension from the two inputs (images and text, respectively) from the middle layer. By calculating the distance between these feature vectors, we analyzed the relationship between the number of likes and RTs. The results revealed that the average vectors of BERT and inceptionresnetv2 served as predictors of the number of likes and RTs. We also found that tweet text with a low number of likes and RTs was short and simple.


Construction of MBTI Personality Estimation Model Considering Emotional Information

Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation (PACLIC35)

The widespread use of social networking services (SNSs) such as Twitter and Facebook in recent years have rendered it easy to interact with various people all over the world.Analysis of the user personality and emotion using features such as the text information and image information on SNSs has been researched.This study analyzes the effectiveness of using emotional information as a feature when building a personality estimation model for Twitter users. The extensively used Myers-Briggs type indicator (MBTI) is used for analysis. A method is proposed for estimating the MBTI personality pattern by extracting emotional information from Twitter and adding it to the characteristics of the distributed expressions of sentences and words.Through evaluation experiments, it is determined whether the emotional information is effective in constructing the MBTI personality estimation model. The obtained results demonstrate that the accuracy is improved when emotional information is included as a feature, compared to the case without emotional information.


Construction of Emotional Similarity Word Embedding Model for Sentiment Analysis

20th Mexican International Conference on Artificial Intelligence (MICAI2021)

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


The Relationship between Personality Patterns and Harmfulness: Analysis and Prediction based on Sentence Embedding

3rd Technological Competency as Caring in the Health Sciences 2021

In this paper, we propose a method for detecting harmful contents in social media. In the Social Networking Service (SNS) the articles and messages posted on these services can be viewed by an unspecified number of unknown people from worldwide. Recently, increasing the number of users who have been forced to close their SNS accounts or have been suffering from mental problems because of blew up SNS triggered by hate speech or bullying remarks in social media. Even if the management of the SNS takes countermeasures, it is difficult to deal with all types of harmful expressions. In this research, we focus on the characteristic that harmful statements need to be judged from the entire sentence, and extract features of harmful expressions using a general-purpose language model.


Lifestyle Analysis via a Corpus of Disease-Fighting Weblogs

Proceedings of 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML 2021)

In recent years, the population of pre-diabetics in Japan has been increasing year by year. Type 2 diabetes is a type of lifestyle-related disease that can be prevented to a certain extent by correcting lifestyle habits. However, by the time we realize that there is a problem with our lifestyle, it may be too late. Therefore, early detection of risk factors for lifestyle-related diseases is important. In this study, we collected the blogs of lifestyle-related disease fighters, set up multiple keyword categories that are considered to be related to risk factors, and constructed a corpus of disease fighting blogs with labels for each category. The results of the evaluation experiments show that our proposed method can be applied to a wide range of topics. As a result of evaluation experiments, our proposed method achieves categorization of keywords and sentences with higher accuracy than the simple method.


Visualization of the Artist Relations Using Twitter User Profiles

Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI,


ASCII Art Classification Model by Transfer Learning and Data Augmentation

Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI

In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also usedVGG16, ResNet-50, Inception v3, and Xceptions pre-trained networks to fine tune our categorization. As a result of the experiment of fine tuning by VGG16and data augmentation, an accuracy rate of 80% or more was obtained in the human category.


Keyword Extraction from TV Program Viewers Tweet Based on Neural Embedding Model

In recent years, young people have not been watching television (TV) as much as they used to. This is mainly because a number of TV programs are very long and/or have limited viewing times. Recently, individuals have been actively posting live-action tweets on Twitter to comment on TV content while watching programs in real time. In this study, we propose a method for extracting key phrases related to the event scenes of TV programs using live tweets, and we propose a scene search system that aims at efficient TV program viewing. The experimental results indicated that the program contents were estimated with an error of approximately 5% to 10% with respect to the program time. In addition, the extracted key phrases were visualized for each event scene category using the t-SNE algorithm.


Towards Automatic Dental Diagnosis System Based on Deep Learning

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


Reverse Image Search for the Fashion Industry Using Convolutional Neural Networks

2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP)


Construction of Annotated TOBYO Blog Corpus for Lifestyle Disease Analysis of Diabetic Patient

Proceedings of The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE20)

In this study, we target to analyze weblogs authored by diabetes patients describing their struggle against their disease. In order to build a corpus for analysis, we annotate tags to the important keywords/key phrases such as blood glucose level, diet and emotions in collected weblog data. We aim at constructing a labeled lifestyle disease weblog corpus (called as TOBYO weblog corpus) to extract useful information from patients' weblogs of against illness. For example, to improve our lifestyle, it would be very useful to know how we should live every day, what kind of diet we should eat, how much exercise is appropriate, and so on. In our study, we organize the tagged data and take statistics on part-of-speech combinations. We set the top N thresholds in the rank of the frequency of the part-of-speech and create a system to exclusively pre-process the key phrases that are ranked as more frequent part of speech combinations. The extracted key phrase information is vectorized by a model of BERT (bidirectional encoder representation from a transformer), then the key phrases are sorted out by making a model for category prediction that uses two inputs (BERT vector and character frequency vector). To evaluate our proposed method, we conduct cross validation test and open test by using several machine learning algorithms. In comparison with the baseline classification method, which is a simple and ordinal neural network method, our method can achieve higher performance.


Construction of lifestyle disease weblog corpus and evaluation of keyword classification model

International Forum on Advanced Technologies 2020 (IFAT2020)


Emotional State Estimation by Dialogue History and Sentence Distributed Representation

Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2019)

The paper presents a novel approach to predict humans emotion in the dialogue. We target the scenario dialogue corpora. The corpora are annotated by some subjects with emotion tag. We use sentence embedding by Bidirectional Encoder Representations from Transformers as versatile and rich feature. By acquiring multi-context information from dialogue text, the accuracy of emotion prediction can be improved. As an experiment by machine learning algorithm such asdeep neural networks for the proposed method, we achieved higher accuracy than the method using embedding feature from utterance itself.


Image Generation Method Based on Improved Condition GAN

International Conference on Systems and Informatics (ICSAI2019)


Kansei Mapping of Color Images Using Earth Mover's Distance

International Conference on Software Technology and Engineering (ICSTE2019)


An Approach for Conversion of Japanese Emoticons into Emoji Based on Character-Level Neural Autoencoder

Frontiers in Artificial Intelligence and Applications

In this paper, we propose a method for converting Japanese emoticons into emoji. The method creates a model that translates text into emoji using training neural networks with a character-based feature; the model determines the positive/negative/neutral polarity of the emoji from the text. By extracting a feature vector from the hidden layer of the model, we calculate the similarity between the input sentence and the text annotated with emoji in the database; the conversion candidates come from the emoticon in the input text. In comparison tests of the proposed method and a word-level feature method that uses a word distributed representation vector, the emoji emotion polarity-based model achieved a maximum accuracy rate of 90.0%, representing an 8% improvement over the word-level feature method.


Emotion Analysis on Weblog of Lifestyle Diseases

Proceedings of the 2019 International Symposium on Signal Processing Systems (SSPS2019)

In this paper, we investigate the relation between patient lifestyle and emotional state transition by analyzing weblog articles written by patients with diabetes. To conduct our analysis, we use blood sugar level and body weight to indicate the disease condition, together with meal keywords and sentences that express feelings in the selected weblogs. A corpus annotated with a tag to word units in the target articles is constructed and a method of keyword classification based on the constructed corpus is proposed and evaluated. Finally, we analyze whether the indicated emotion is positive or negative in sentences expressing emotion in the corpus and investigate the relation between the emotion and a change in blood sugar level.


Analysis of Reply-Tweets for Buzz Tweet Detection

Proceedings of 33rd Pacific Asia Conference on Language, Information and Computation (PACLIC33)

In this study, we propose a method for predicting whether a tweet will create a buzz on the Internet by examining tweeted replies posted by others. We also investigate the distinguishing characteristics of replies to buzz tweets by analyzing feature amounts. Our proposed method first converts each reply tweet into a vector expression using a word distributed representation or some other vectorization method. We then apply a machine learning method for binary classification todetermine whether the reply is to a buzz tweet or a non-buzz tweet. We classify the target tweet into buzz or non-buzz categories by comparing the total buzz and non-buzz scores produced by the classifier. The proposed method using StarSpace achieved 93.1% F1-score, while an approach that used number of retweets and number of favors (likes) achieved 77.8% F1-score. Wealso found that there are a number of words that are characteristic of buzz tweet replies anda number of words that are characteristic of non-buzz tweet replies.


Personality Analysis on Dialogue System for Emotional Interaction Robot

Conference Proceedings of Asia Pacific Society for Computing and Information Technology 2019 Annual Meeting (APSCIT 2019 Annual Meeting)

In this study, we proposed the personality analysis method for dialogue system towards realization of emotional interaction robot. To achieve smooth communication between human and robot in dialogue, it is necessary that overcome feeling of strangeness by generating dialogue utterance based on consistent personality. Firstly, we extract the wording feature from utterance texts on social media. To obtain relation between the wording feature and personality ego-state level, we use sentence level embedding feature and create state level estimator for each ego-state. Next, we evaluate the ego-state level estimator with an evaluation test set. Finally, we use this estimator for analysis of chat-bot dialogue. By comparing the existing chat-bot dialogue and user utterance, we consider about non-reality of current chat-bot dialogue generation.


Research on statements in tweets and sleep time of classification

the 3rd International Conference on Natural Language Processing and Information Retrieval


Factor Affecting Low-Arch and High-Arch of Foot

13th International Society of Physical and Rehabilitation Medicine (ISPRM 2019)


Body Factors of Low Arch of Foot for Middle and Senior Age

World Confederation for Physical Therapy (WCPT2019)


Extended Dataset for Food Image Classification Based on Twitter

Hong Kong International Conference on Engineering and Applied Science (HKICEAS 2018)


An object Detection System Based on YOLOv2 in Fashion Apparel

2018 IEEE 4th International Conference on Computer and Communications (ICCC)


Modeling Relations Between Profiles and Texts

Proceedings of the 14th the Asia Information Retrieval Societies Conference (AIRS 2018), LNCS 11292

We propose a method to model Twitter texts and user profiles simultaneously by considering the relations between the texts and profiles to obtain the distributed representations of the words in both.


ASCII Art Category Classification based on Deep Convolutional Neural Networks

Proceedings the 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

In text-based communications on social media, emoticons, emojis, and stamps provide important ways to convey emotion and facilitate smoother communication in a short text. As non-verbal expressions, the characters in ASCII Art have no inherent meaning. However, by using a characters position or shape, emotion or intention can be expressed visually. Until now, it has been difficult to classify ASCII Art using the ASCII Art characters. In this paper, we propose a method for classifying ASCII Art by converting ASCII Art into images and using deep learning. In an evaluation experiment, the proposed method was able to achieve a classification success rate as high as 97%.


Transfer Learning Based on Utterance Emotion Corpus for Lyric Emotion Estimation

Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

In prior research, there are various emotion estimation approaches that are corpus-based, dictionary-based, or rule-based. However, it is necessary to prepare language resources suitable to each domain, which will incur substantial cost. In this paper, we propose an approach to estimate emotion of lyric phrase by using transfer learning. We applied the existing utterance emotion corpus to the lyric emotion estimation task. As the result of an evaluation experiment, we obtained higher accuracy than the baseline method using a simple bag of words feature tf-idf weighted vector and logistic regression model.


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

2018 10th International Conference on Software Technology and Engineering


Emoticon-based emotion analysis for Weibo articles in sentence level


Classification of Emoji Categories from Tweet Based on Deep Neural Networks

Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval (NLPIR2018)

In this paper, we describe the sentiment analysis method from tweets based on emojis category. Many of existing study about sentiment analysis focused on the emotional expressions included in sentence. However, because there are various kinds of emotional expressions, such as Internet slang, it cannot be constructed that the fixed emotional expression dictionary. The most of the methods based on corpus and machine learning, its performance is quite depended on the quality of annotation. Therefore, we attempt to use categories which are expressed by emoji as sentiment label instead of manually annotated labels. Our proposed method uses automatically annotated category label by emoji which is annotated to sentence, and train word embedding feature by deep neural networks. As the result of the experiment, our proposed method overcome the simple word feature based method.


Emotion Recognition from Emoticons using Convolutional Neural Networks

Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018

In recent years, text communication has been grown steadily so that all kinds of daily contacts could be conducted now through Internet based text messaging services. As an advantage of text-based communication via the Internet, we can quickly interact with anyone wherever we are. At the same time, since it is difficult to express emotions with each other only with text, the chance of adding non-verbal information such as photos to text is increasing. Among the non-verbal information, there are emoticons in expressions that have been developed by Internet bulletin boards, chat, e-mail, etc. In particular, the types of emoticons used in Japan are diverse, even now; various types of emoticons continue to increase. There have been studies on these emoticons until now, which have become increasingly important in recent years. In this paper, we propose a method to classify facial expressions expressed from emoticons by letting deep convolutional neural networks. When recognizing emotions from emoticons, it is not known that seeing emoticons in character units is very effective. Information such as role positioning and coexistence roles is necessary. Characters in emoticons differ depending on font it also gives different impressions depending on context. In order to realize semantic analysis / emotion analysis considering emoticons, it is necessary to provide a corpus that records various kinds of emoticons and a database that gives information such as meanings and emotions expressed by emoticons. However, including all the emoticons is difficult, and since the emoticons are more vague than ordinary language expressions, that is why the construction of the dictionary is delayed. In this paper, we propose a method to classify the emotions of the emoticons by letting the deep convolutional neural network learn the features of the emoticons. By treating the emoticon as an image, the visual features can be captured and it is considered that natural recognition can be performed instead of processing like a character string. In addition, since the emoticon characterizes the facial expression with a character string, there is a problem that the impression is different only when the used fonts are different from each other, and therefore, when different fonts are used, how the classification accuracy is changed, we consider whether to do so.


Sentence Emotion Classification for Intelligent Robotics Based on Word Lexicon and Emoticon Emotions

Proceedings of the International Conference of Intelligent Robotic and Control Engineering


Visualization of the occurrence trend of infectious diseases using Twitter

Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC 2018), (poster)

We propose a system for visualizing the epidemics of infectious diseases. We apply factuality analysis both to disease detection and location estimation for accurate visualization. We tested our methods for several infectious diseases, and show that our method performs well on various diseases.


Predicting facial expression from emoticon based on convolutional neural networks

4th International Forum on Advanced Technologies (IFAT 2018)

Text communication has been developed in recent years, all kinds of daily contacts are through the SNS regardless of age, gender and active. As an advantage of text-based communication via the Internet. There is a point that you can quickly respond to anyone wherever you are. In this paper, by letting the deep convolution neural network learn the image features of emoticons. We propose a method to classify by emotion expressed by emoticons. By treating emoticons as images, it is possible to capture visual features and think that natural identification can be made rather than handling as character strings.


The Effectiveness of Image Data Augmentation for Deep Learning

Conference Abstract of the 3rd International Conference on Multimedia and Image Processing


Visual Similarity for ASCII Arts Using Image Features

Proceedings of the 2018 2nd International Conference on Machine Vision and Information Technology (CMVIT 2018)

Communication with various types of non-verbal expressions has been increasing on Social Networking Services. Among them, ASCII art is a character-based design technique to express human emotions. In many cases, ASCII arts are generated from original images by mapping some region of pixels to a certain character. In the field of natural language processing, ASCII arts are treated as sequences of characters. In addition, there have been few studies that aim to analyze or classify ASCII arts. In this paper, we extract characteristics of ASCII arts using image shape features. Our image shape-based features provide visual similarity between ASCII arts that are invariant across image scale and characters used in ASCII arts. Moreover, the proposed method enable us to compare visual similarity between ASCII arts and original images. To extract shape features from ASCII arts, we use image features of original images by converting ASCII arts to images. In the experiment, we compared visual similarity between ASCII arts and original images by using Histograms of Oriented Gradients(HOG). Our experimental result shows that the proposed method is effective to compare visual similarity between ASCII arts and original images.


Exploring the Relations Between Posts and Comments for Short Text Conversation

Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17)

Chinese Association for Artificial Intelligence


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

Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17)

In this paper, we proposed a model to classify reviews based on review data of different media. Recently, researches have been actively made on transfer learning between different domains with various kinds of big data as a target. Evaluation expressions are usually different in different domains, and this becomes a barrier for reputation analysis. Users usually use different linguistic expressions to refer to the creative works of each media. For example, media such as "anime", "comics", "games" and "movies" have different terms or expressions to be described their creative works. These differences can be considered showing features of each medium and such differences should be found in other expressions as well as in evaluation expressions. We analyzed what effect such differences would cause to classication accuracy by conducting transfer learning between review data ofdifferent media. In this paper, we proved compatibility between original (pre-transferring) and target (post-transferring) media for each medium of the creative works by constructing a review classication model. As a result of evaluation experiments, we could more accurately estimate review score without using SO-Score for training of review fragment based on Long Short Term Memory (LSTM) than the SO-Score based method.


Index Generation of BGM Video Based on Distinctive Comments

The purpose of this study is to generate indexes to the BGMs based on the distinctive comments annotated to the BGM videos posted on Nico Nico Videos. Our proposed method detects the end/start positions of the BGMs considering the increase/decrease of the number of the comment data, the comments distinctive to the songs and the exclamatory expressions annotated to the videos. The evaluation result showed that the proposed method could generate correct indexes within 10 seconds error for the BGM videos with length of less than 40 minutes and with annotation of over 30,000 comments.


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

In this study, we focus on a representative SNS, Twitter and target emotion estimation from tweets posted on Twitter. we construct emotion estimation models applicable to each gender. It is considered that there would be different word usage tendencies depending on gender. Therefore, we analyze what difference exists in usage tendency of slangs or emoji, which are thought to be related to emotion, depending on gender. Then, we propose a method to improve emotion estimation based on neural networks by usingdistributed representation models for each gender. As the result of the evaluation experiments, the higher estimation accuracy was realized by training with Deep Convolutional Neural Networks using word's distributed representation as feature than by training with Feed Forward Neural Networks.


TUA1 at NTCIR-13 Short Text Conversation 2 Task

Proceedings of the 13th NTCIR Conference on Evaluation of Information Access Technologies


Facial Expression Classification Based on Shape Feature of Emoticons

Proceedings of 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017)

Emoticons are used in the situation of textual communication such as web mails and internet forums. Many of the existing studies dealing with classification or extraction of emoticons regard emoticons as a kind of character string and focus on what characters constitute the emoticons or how they are lined up. However, emoticons are used to express human facial expressions, and characters constituting them represent various facial parts such as eyes, nose, mouth, etc. Such characters can be identified as different facial parts depending on their positions, and facial expressions are thought to be represented by the combinations of their shape features. In this study, we classified the facial expressions of emoticons by focusing on the shape features of those emoticons. To deal with shape features of emoticons, we converted emoticons, which are text data, to image data. Emoticons are mainly formed by line segments of characters, and use only black and white colors. Therefore, other factors such as colors and shades were not considered as the feature to classify the facial expressions. In the experiments, we used image features that did not require color information. As the result of comparative experiment with the 1-nearest neighbor method using character features, the facial expression recognition rate is 52% when using the Histograms of Oriented Gradients(HOG) was used as image feature. By this result, proposed method improved recognition rate by 2 % than using baseline.


Gender Estimation from Facial Images by Deep Neural Network

Proceedings of Annual Conference on Engineering and Applied Science

In this paper, we collect new learning data and learned this by the existing neural network in order to exceed the accuracy of the model of the existing research. As a result, we succeeded in improving the accuracy by increasing the learning data. However, we discovered that performance declined due to biased learning data. The future task is to further improve accuracy and Research the relationship between bias and accuracy of learning data.


Emotion Recognition of Emoticon Based on Character Embedding

9th International Conference on Software Technology and Engineering (ICSTE2017)

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


Face Identification Based on Deep Convolutional Neural Networks

9th International Conference on Graphic and Image Processing


Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation

Book of Abstracts Proceeding of Engineering & Technology, Computer, Basic and Applied Sciences (ECBA-2017)

In recent years, flaming on social media has been a problem. To avoid flaming, it is useful for the system to automatically check the sentences whether they include the expressions that are likely to trigger flaming or not before posting messages. In this research, we target two harmful expressions. There are insulting expressions and the expressions that are likely to cause a quarrel. Firstly, we constructed an original harmful expression dictionary. Because a large cost requires to collect the expressions manually, we constructed the dictionary semi-automatically by using word distributed representations. The proposed method used distributed representations of the harmful expressions and general expressions as features, and constructed a classifier of harmful/general based on those features. An evaluation experiment found that the proposed method could extract harmful expressions with accuracy of approx. 70%.On the other hand, it was found that the proposed method could also extract unknown expressions, however, it wrongly extracted non-harmful.


Distributed Representations for Words on Tables

Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), Part-I (LNAI 10234)

We consider a problem of word embedding for tables, and we obtain distributed representations for words found in tables. We propose a table word-embedding method, which considers both horizontal and vertical relations between cells to estimate appropriate word embedding for words in tables. We propose objective functions that make use of horizontal and vertical relations, both individually and jointly.


Acceleration of Similar Word Search in Distributed Representation

Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2017) (poster)

We report our study on accelerating search for similar words in distributed representation using cosine similarity measure.Our main idea is to convert similarity value to Euclidean distance, and apply the branch-and-bound approach to distance calculation itself, which means to terminate distance calculation when the current value exceeds the current N-th best similarity value when N is the size of final list of words similar to the query.We also apply other techniques for speeding up such as converting vector representation using PCA and reduction of adding and multiplying operations in distance calculation.


A Comparison of Face Recognition Features Derived from Convolutional Neural Networks

Conference Abstracts of the 2017 International Conference on Frontiers of Image Processing


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

The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16)


Improvement of the Histogram for Infrequent Color-Based Illustration Image Classification

Fuzzy Systems and Data Mining II, Frontiers in Artificial Intelligence and Applications


Table Topic Models for Hidden Unit Estimation

Proceedings of the 12th Asia Information Retrieval Societies Conference (AIRS2016), LNCS 9994

We propose a method to estimate hidden units of numbers written in tables. We focus on Wikipedia tables and propose an algorithm to estimate which units are appropriate for a given cell that has a number but no unit words. We try to estimate such hidden units using surrounding contexts such as a cell in the first row. To improve the performance, we propose the table topic model that can model tables and surrounding sentences simultaneously.


Effect of Users Attribute on Emotion Estimation from Twitter

Proceedings of the 2nd IEEE International Conference on Computer and Communications

This study focused on emotion estimation fromutterances on Twitter and analyzed the differences caused forestimation according to the attributes of users. SNS userspossess various user attributes. If we could clarify the tendencyfor each attribute to involve emotion estimation, we would beable to create an emotion estimation model suitable for eachattribute. This study considered two attributes, sex and job, andinvestigated the tendency of emotion estimation based on anemotion estimation model constructed by machine learning. Inthis paper, as a feature for machine learning, we used asentence vector that was obtained by the summation of theword distributed expressions. As a result of the evaluationexperiment using a method based on the nearest neighbormethod, we obtained 0.4 higher estimation accuracies thanusing a baseline method based on a simple Bag of Words model.


Judgment of Slang Based on Character Feature and Feature Expression Based on Slangs Context Feature

Communications in Computer and Information Science

This paper proposes a method to distinguish standardwords from slang words using information from the characters comprising thecharacter string. An experiment testing the effectiveness of our method showeda 30 % or more improvement in classification accuracy compared to the baselinemethod. We also use a contextual feature related to emotion to expand theunregistered slang word in the training data into other expressions and proposean emotion estimation method based on the expanded expressions. In our experiment,successful emotion estimation was obtained in nearly 54 % of the cases, anotably higher rate than with the baseline method. Our proposed method wasshown to have validity.


Face Identification based on Deep Learning Using Low Pixel Images

International Conference on Engineering & Technology, Computer, Basic & Applied Sciences


Lyric Retrieval System Based on Word's Intimacy Value


An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language

Proceedings of the 7th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Researches on emotion estimation from text mostly use machine learning method. Because machine learning requires a large amount of example corpora, how to acquire high quality training data has been discussed as one of its major problems. The existing language resources include emotion corpora; however, they are not available if the language is different. Constructing bilingual corpus manually is also financially difficult. Wepropose a method to convert a training data into different language using an existing Japanese-English parallel emotion corpus. With a bilingual dictionary, the translation candidates are extracted against every word of each sentence included in the corpus. Then the extracted translation candidates are narrowed down into a set of words that highly contribute to emotion estimation and we used the set of words as training data. As theresult of the evaluation experiment using the training data created by our proposed method, the accuracy of emotion estimation increased up to 66.7% in Naive Bayes.


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

the 10th International Conference on Natural Language Processing and Knowledge Engineering


Sensibility Estimation Method for Youth Slang by Using Sensibility Co-occurrence Feature Vector Obtained from Microblog

Proceedings of the 2015 IEEE International Conference on Computer and Communications (ICCC2015)

Social networking sites such as Twitter provide more opportunities to express what people think or intend in short text. In short text, abbreviations such as ASAP or joinus and emoticons are often used. Because these expressions are not registered into the existing dictionaries, these are analyzed as unknown expressions. That can be a bottleneck for improving accuracy of reputation analysis in text mining. To use context for unknown word clustering is a major method, however, it usually requires word segmentation process and it has weakness for split errors of unknown expressions such as youth slang. In this paper, we proposed a method to obtain the appropriate context even though unknown expressions cause split errors and estimate sensibility expressed in the text. Because the dimensions of the obtained context vector were enormous, we also proposed a method to create a feature vector based on the co-occurrence of the sensibility words as simple expression with low dimension. As an evaluation experiment, the proposed method showed certain accuracy even with the small training data.


Estimate the Intimacy of the Characters Based on Their Emotional States for Application to Non-Task Dialogue

Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015)

Non-task-oriented dialogue requires someschemes that enable smooth and flexible conversations with auser. For example, it would be possible to go beyond the closedrelationship between the system and the user by consideringthe users relationship with others in real life. In this paper,we focused on the dialogue made by the two characters in adrama scenario, and tried to express their relationship with ascale of intimacy degree. There will be such various elementsrelated to the intimacy degree as the frequency of response to theutterance and the attitude of a speaker during the dialogue. Wefocused on the emotional state of the speaker during the utteranceand tried to realize intimacy estimation with higher accuracy. Asthe evaluation result, we achieved higher accuracy in intimacyestimation than the existing method based on speech role.


Emotion Recognition for Sentences with Unknown Expressions based on Semantic Similarity by Using Bag of Concepts

Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'15)

In studies of emotion estimation from text, varietiesof methods have been attempted such as emotional expressiondictionary or sentence structure dictionary and emotion corpus.However, most of these methods targeted the expressions includedin the existing morphological analysis dictionaries, as a result,they did not pay enough attention to unknown words, especiallynewly coined words. In Japan, the growth of Internet communicationsites such as weblogs and social networking sites broughtyounger people especially in teens and in their 20s to create newwords and use them very often. We prepared an emotion corpusby collecting weblog article texts including new words, analyzedthe corpus statistically, and proposed a method to estimateemotions of the texts. Most slang such as Youth Slang is tooambiguous in sense classification to be registered into the existingdictionaries such as thesaurus. To cope with these words, wecreated a large scale of Twitter corpus to calculate sense similaritybetween words. We proposed to convert unknown to sense classid to process the words that were not included in learning data.We defined this as a method using Bag of Concepts as feature. Asa result of the evaluation experiment using several classifies, theproposed method was proved robustness for unknown expression.


Extracting Corpus-Specific Strings by Using Suffix Arrays Enhanced with Longest Common Prefix

Proceedings of the 10th Asia Information Retrieval Society Conference (AIRS 2014), LNCS 8870

We propose a new term extraction algorithm that considers all of the substrings as term candidates.Our algorithm uses a suffix array as the data structure that emulates the suffix tree of the corpus.We use two scoring functions, one of which is used to detect good substring boundaries as linguistic chunksand the other is to find domain-specific phrases and combine them with a re-ranking approach.Experiments show that the proposed all-substring term extraction algorithm shows good performancefor highly-frequent terms compared with the baseline algorithm that uses a morphological analyzer in the preprocessing step.


Reranking the Search Results for Lyric Retrieval Based on the Songwriters' Specific Usage of Words

Electronics, Communications and Networks IV Proceedings of the 4TH Internationa Conference on Electronics, Communications and Networks, 12 15 December 2014, Beijing, China

In this paper, we proposed a method to search lyrics by considering tendency of the words used by the songwriters, and aimed to improve the search accuracy. The existing methods of query expansion for document retrieval tended to focus on the words co-occurrence relation obtained from large amount of text data or to focus on synonymy relation of words to expand query. However, lyrics have different characteris-tics from business documents or weblog articles. For example, lyrics contain a lot of expressions made with different combinations of words compared to other text styles. Therefore, it is not always effective to focus on co-occurrence relation or semantic relation on thesaurus. In this paper, we were on the assumption that the tendency of the words used in the lyrics are different among songwriters. And, we constructed a data of the important words for each songwriter. We used this important word list made for each songwriter to add weight on the search results by n-gram. According to the weights, we rearranged the rank order of the search result aiming to improve the accuracy. We conducted an evaluation experiment using the artificially-created fragment of lyrics including errors as search query, and found that the proposed method improved MRR compared to the baseline method.


Emotion Predicting Method Based on Emotion State Change of Personae according to the Other's Utterance

Proceedings of the 3rd IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2014)

It is a very difficult task to predict others emotion from dialogue flow for dialogue system or robot, however, that is a natural task for human.As the reason, because of the emotion of human changes at all times when he / she have a conversation with other people, these changes would be considered that oc-curred by responding internal emotional state to stimulus from external world .In the researches about dialogue system, most of them are superficial feature of utterance for question-answering system, thus, internal emotional state and stimulus from external world.In this paper, we focused on the scenario dialogue sentences. We annotated the various information to the corpora for helping analyze dialogue flow or human relationships and constructed the scenario emotion corpus. We discussed that the others utterances or emotions how effect to emotion changes by analyzing this cor-pora.As the method to predict the others emotion changes from self-emotion, we proposed the predicting method based on the emotion predicting weight. As the result of evaluation experiment, the effectiveness of the proposed method could be confirmed.


Unsupervised Analysis of Web Page Semantic Structures by Hierarchical Bayesian Modeling

Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014, Part II, LNAI 8444

We propose a Bayesian probabilistic modeling of the semantic structures of HTML documents.We assume that HTML documents has logically hierarchical structures and model them as links between blocks.These links or dependency structures are estimated via sampling methods. We use hierarchical Bayesian modeling where each block is given labels like ``heading''or ``contents'', and words and layout features (i.e., symbols and HTML tags) are generated simultaneouslybased on these labels.


Identifying who drew the illustration focusing on the eyes of the characters

Proceedings of 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision

In this paper, we aimed to automatically identify who draw the of illustration image. Designs of illustration image differ depending on the illustrators. Also, it is difficult to find out common characteristics from the several illustrations drawn by illustrator. We tried to identify who drew the illustration by detecting distinctive habits of the illustrator from his/her works and by using them as the feature. To obtain the feature, we focused on the area of eyes of the illustrated characters. We obtained the edge information from there and calculated percentage of the edges. We call this percentage a "Line Percent" and considered it as the unique feature quantity of the illustrator. From an experiment, the average recall was 35 % and some of the recalls were over 90%. This result suggested that we could identify who draw the illustration images to some extent by using Line Percent as the feature.


Improvement of Indexing Methods for Audio Fingerprinting Systems

International Conference Data Mining, Civil and Mechanical Engineering (ICDMCME'2014)

In music retrieval methods based on audio fingerprints, a large database is required in order to compare the fingerprints extracted from the query. It is necessary to develop an efficient search method. In this paper, we propose a method for index compression using a compressed suffix array. The proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. We also use Fibonacci Hashing function which can adjust the size of hash table according to the capacity of memory, provide a good distribution of hash value, and save memory.


A Fast Retrieval Algorithm Based on Fibonacci Hashing for Audio Fingerprinting Systems

Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science

In audio fingerprinting system, the database consists of hundreds of millions of sub-fingerprints. How to find out the most similar audio in the shortest time and use less memory in huge repository of sub-fingerprints is a hot topic of research. Recently, Philips introduced an effective search method based on hash table. In this paper, a fast retrieval algorithm based on the Fibonacci Hashing is proposed as an extension of Philipss method. The algorithm uses Fibonacci Hashing function which can adjust the size of hash table according to capacity of memory, provide a good distribution of hash value, and save memory. The performance of presented algorithm has been evaluated by experiments.


Emotion recognition method based on normalization of prosodic features

Proceedings of the APSIPA Annual Summit and Conference (ASC) 2013

Emotion recognition from speech signals is one of the most important technologies for natural conversation between humans and robots. Most emotion recognizers extract prosodic features from an input speech in order to use emotion recognition. However, prosodic features changes drastically depending on the uttered text. In order to solve this problem, we have proposed the normalization method of prosodic features by using the synthesized speech, which has the same word sequence but uttered with a neutral emotion. In this method, all prosodic features (pitch, power, etc.) are normalized. However, nobody knows which kind of prosodic features should be normalized. In this paper, all combinations of with/without normalization were examined, and the most appropriate normalization method was found.


Spoken document retrieval using both word-based and syllable-based document spaces with latent semantic indexing

In this paper, we propose a spoken document retrieval method using vector space models in multiple document spaces. First we construct multiple document vector spaces, one of which is based on continuous-word speech recognition results and the other on continuous-syllable speech recognition results. Query expansion is also applied to the word-based document space. We proposed to apply latent semantic indexing (LSI) not only to the word-based space but also to the syllable-based space, to reduce dimensionality of the spaces using implicitly defined semantics. Finally, we combine the distances and compare the distance between the query and the available documents in various spaces to rank the documents. In this procedure, we propose to model the document by hyperplane. To evaluate our proposed method, we conducted spoken document retrieval experiments using the NTCIR-9 SpokenDoc data set. The results showed that using the combination of the distances, and using LSI on the syllable-based document space, improved retrieval performance.


Spoken Content Retrieval Using Distance Combination and Spoken Term Detection Using Hash Function for NTCIR10 SpokenDoc2 Task

In this paper, we describe a spoken content retrieval (SCR) and a spoken term detection (STD) which were used in the 2nd round of the IR (Information Retrieval) for Spoken Documents (SpokenDoc2) task. The SCR method maps the target documents into multiple vector spaces, which are the word-based vector space constructed by the word-based speech recognition results and the syllable-based vector space constructed by the syllable-based speech recognition results, by a vector space model. In the STD method, the sub-sequences extracted from target document are converted into the bit sequences by using the hash function. A query is also converted into a bit sequence by the same way. The candidates are detected by calculating hamming distance between the bit sequence of the query and that of target documents.


A Joint Prediction Model for Multiple Emotions Analysis in Sentences

14th International Conference on Intelligent Text Processing and Computational Linguistics

In this study, we propose a scheme for recognizing people's multiple emotions from Chinese sentence. Compared to the previous studies which focused on the single emotion analysis through texts, our work can better reflect people's inner thoughts by predicting all the possible emotions. We first predict the multiple emotions of words from a CRF model, which avoids the restrictions from traditional emotion lexicons with limited resources and restricted context information. Instead of voting emotions directly, we perform a probabilistic merge of the output words' multi emotion distributions to jointly predict the sentence emotions, under the assumption that the emotions from the contained words and a sentence are statistically consistent. As a comparison, we also employ the SVM and LGR classifiers to predict each entry of the multiple emotions through a problem transformation method. Finally, we combine the joint probabilities of the multiple emotions of sentence generated from the CRF-based merge model and the transformed LGR model, which is proved to be the best recognition for sentence multiple emotions in our experiment.


Spoken document retrieval using combinational use of distances of multiple vector spaces and query expansion with optimized weight parameters

We propose a distance combination method for spoken document retrieval. The proposed distance combination method maps the target documents, which are transcription of speech made by a speech recognizer, to multiple vector spaces. We, then, calculate the distance between a query vector and multiple versions of the documents on each vector space and combine these distances for retrieval. In addition, we propose a novel weight parameter determination method for query expansion, to minimize an angle between an expanded query vector and a document vector. The proposed methods achieved the best retrieval performance on a SpokenDoc task in an NTCIR-9 workshop.


Recognition of Multiple Emotion States of Words using Multi-engine System

11th International Conference on Computer Applications 2013

In this paper, we propose a multi-engine voting system to recognize the multiple emotion states expressed in the Chinese words according to the word identity and the context information, which can better reflect the people's real emotion states compared to previous works focused on single emotion analysis. Conditional Random Field and Maximum Entropy are employed to learn two probabilistic models to predict the possible emotions and assign the corresponding probability scores to the eight basic emotion types as well as the no-emotion type. To solve the problem of imbalanced data source, we introduce a threshold to improve the recognition of no-emotion and other word emotions. Our experiment achieves promising results which prove that multiple emotion states can be better recognized by the multi-engine system rather than either Conditional Random Field or Maximum Entropy


Emotion Estimation from Sentence Using Relation between Japanese Slangs and Emotion Expressions

Proceedings of 26th Pacific Asia Conference on Language,Information and Computation

Most of Japanese slang words such as Wakamono Kotoba are analyzed as unknown word or segmented wrongly by the morphological analysis system. These problems are causing negative effect on sentiment analysis in text. These words generally have many varieties of notations and conjugations, and they lack versatility. As a result, many of them are not registered in the dictionaries, making morphological analysis more difficult. In this paper, we aimed to decrease such negative effects of Wakamono Kotoba for the accuracy of emotion estimation from sentence and proposed a method to increase the accuracy by using a classification method based on machine learning. In this method we used emotional expressions which had high relevance with Wakamono Kotoba as feature. As a result, the proposed method obtained 20% higher accuracy than the method only using morpheme Ngram as feature.


Judgment of Depressive Tendency from Emotional Fluctuation in Weblog

Proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2012

Some people who have tendency toward depression describe their inner thoughts vividly in their weblogs. Such descriptions are sometimes made consciously but at other times unconsciously by the authors. If such unconscious authors could become aware of their own symptom of the depression, that would contribute to early detection and treatment of the sickness. To achieve this, it is essential to find out the tendency of depression from the descriptions in weblog. Because the symptom of depression is not static, we attempted to detect the tendency of depression focusing on the temporal fluctuation of emotion of the authors. As many weblogs describe what happened or what the authors felt in daily life like a personal journal, we thought that weblogs were ideal for obtaining the time-varying emotion of the authors. In this paper, we proposed the method to detect the tendency of depression in weblog. Our method is based on the temporal fluctuation of emotion described by the authors.


Learning Distance Metrics with Feature Space Performance for Image Retrieval

2012 Third International Conference on Mechanic Automation and Control Engineering (MACE '12)


Skeleton Extraction and Pruning based on Multi-scale Turning Angle Descriptor

18th Korea-Japan Joint Workshop on Frontiers of Computer Vision

The skeleton is an important feature in the representation of a shape in image analysis. This paper includes two parts. First, we propose an Euclidean distance-ordered thinning algorithm for skeleton extraction. Second, since the skeleton is sensitive to an object's boundary noise, we propose multi-scale turning angle descriptor to locate the salient skeleton points on the boundary, and we could cunduct the skeleton pruning by the salient points.


Region-Based Image Annotation Using Gaussian Mixture Model

2012 International Conference on Information Technology and Software Engineering


Exploring Emotional Words for Chinese Document Chief Emotion Analysis

Proceedings of 25th Pacific Asia Conference on Language, Information and Computation(PACLIC2011)

In this study, we develop a document emotion analysis model by making use of the function of emotional words annotation. Eight basic emotion categories have been selected, including Expect, Joy, Love, Surprise, Anxiety, Sorrow, Anger and Hate, for both the word and the document level emotion analysis. We introduce two parameters, term relevance and term frequency, to evaluate the relations between the word emotions and the document emotions. Promising experimental results reveal the effectiveness of our document emotion analysis model under different emotion situations.


The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25)


Emotion Estimation of Wakamono Kotoba Based on Distance of Word Emotional Vector

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

Recently, there are many research that aim at estimating emotion from text. The meaning of language expression that we use in daily life varies depending on the context used. In that sense, language expression conveys ambiguous information. Wakamono Kotoba are words used by young people, and they also have semantic ambiguity. Most of the Wakamono Kotoba are not registered in the existing dictionaries, making it difficult for a system to recognize the meaning of Wakamono Kotoba. In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applied Earth Movers Distance to vector of words. As a result of the evaluation experiment, higher estimation accuracy was obtained by considering emotional distance between words -- an approach that had not been used in the conventional research -- than by using only word importance value.


Modification Relations based Emotional Keywords Annotation using Conditional Random Fields

The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011)


Fast Hamming Space Search for Audio Fingerprinting Systems

Proc. International Conference on Music Information Retrieval

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


Modification Relations Based Emotional Keywords Annotation Using Conditional Random Fields

Proceedings of 2011 Fourth International Conference on Intelligent Networks and Intelligent Systems

In this study, we demonstrate the effectiveness of modification features for emotional keywords annotation on Chinese sentences extracted from a Chinese emotion corpus. Eight basic word emotion categories have been selected and Conditional Random Fields is employed in eight experiments with different sets of features. Our result shows that modification features make the contribution to emotional keywords annotation.


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

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR 2011)

VP-tree is an efficient metric space indexing method, however the number of distance calculations at leaf nodes tends to increase. In this paper, we propose an efficient algorithm to reduce the number of distance calculations at leaf nodes of the VPtree. The conventional VP-tree uses the triangle inequality at the leaf node in order to reduce the number of distance calculations. At this point, the vantage point of the VP-tree is used as a reference point of the triangle inequality. The proposed algorithm uses the nearest neighbor (NN) point for the query instead of the vantage point as the reference point. By using this method, the selection range by the triangle inequality becomes small, and the number of distance calculations at leaf nodes can be cut down.


Music Retrieval Method Based on Filter-bank Feature and Earth Mover's Distance

7th International Conference on Natural Computation

In this paper, we propose a novel music retrieval method using a filter bank feature and earch mover's distance (EMD). In the proposed method, we use MFCCs for acoustic features and EMD for a distance measure. Evaluation experimental results show that the accuracy of the proposed method achieves 96.73%.


Security Clustering Algorithm Based on Reputation in Hierarchical Peer-to-Peer Network

4th IEEE International Conference on Computer Science and Inforymation Technology


Optimum Distance Metric Based on Image Feature Space

4th IEEE International Conference on Computer Science and Information Technology


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

2011 International Workshop on Nonlinear Circuits, Communication and Signal Processing

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


Instance Search Experiments for TRECVID 2010

Proceedings of TREC Retrieval Evaluation 2010 (TRECVID2010)

On TRECVID 2010, we have participated in the instance search (INS) pilot task. For the INS task, our main focus was to apply SIFT-based image retrieval method with facial images. The overview of our retrieval system is described as the below. At the registration phase, facial images are extracted from each cut frame using the Haar-like operator. Next, noise images in the extracted facial images are eliminated using the SVM. Finally, SIFT features are detected from true facial images, and these features are registered in the facial database. At the retrieval phase, the facial area of the query image is extracted using the same operator as the registration phase. Next, SIFT features are detected from the query facial image. After that, the similarity between query facial image and the facial database is calculated based on 128 dimensional SIFT features.


A Method to Improve Metric Index VP-tree for Multimedia Databases

Proc. of 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2011)


First Coming Clustering Algorithm Based on Reputation in Peer-to-Peer Network

2010 WASE International Conference on Information Engineering


Euclidean Distance-Ordered Thinning for Skeleton Extraction

Proc. of the 2nd International Conference on Education Technology and Computer, ICETC'10

The skeleton is an important feature for the representation of a shape in image analysis. In this paper, we propose a novel Euclidean distance-ordered thinning algorithm for skeleton extraction. The experiment results show that the algorithm is able to extract the connected and one-pixel wide skeleton that can correctly preserve the topology of the object. Furthermore, the extracted skeleton locates on the accurate position and it is insensitive to boundary noise.


A Skeleton Extraction and Pruning Algorithm Based on Bisector Angle

16th Korea-Japan Joint Workshop on Frontiers of Computer Vision

The skeleton is an important feature in the representation of a shape in image analysis. This paper includes two parts. First, we propose a fast, efficient and accurate skeleton extraction method based on the criterion which can be used to determine whether a given pixel is a skeleton point independently. Second, since the skeleton algorithm is sensitive to an object's boundary noise, we propose a pruning method based on bisector angle, which can completely remove redundant protrusions and obtain accurate skeleton and preserve the original topological property of a shape.


A Research of Distance Transform based Peeling Algorithm for Overlapped Fibers Separation

International Conference on Computational Intelligence and Software Engineering

Overlapped fiber separation is one of main procedures in cross-sectional fiber analysis. In the proposed distance transform based peeling algorithm, a separation point is determined by removing points with the least distance. The overlapped fibers in cross-section are separated by the separation point. Experiments show that the proposed algorithm precisely separates overlapped fibers of various shapes.


A Fast Retrieval Algorithm for the Earch Mover's Distance Using EMD Lower Bounds and the Priority Queue

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


Two-Level Content-based Endoscope Image Retrieval

Fourth International Conference on Natural Computation

Based on the analysis of endoscope image, in this paper, a new color quantification method is proposed to extract improved CCV and the V component shape invariant moment achieving image feature base.


A Fast Retrieval Algorithm for the Earch Mover's Distance Using Lower Bounds

IEEE International Conference on Information Reuse and Integration

The Earth Mover's Distance (EMD) was introduced in Computer Vision as better distance measure between two distributions, and has been widely used in multimedia databases. Rubner et al. have been proposed CBIR model using the EMD, and its high quality for image similarity search has been demonstrated. Formally, the Earth Mover's Distance is defined as a Linear Programming problem which can be solved using the simplex method. Its computation, however, is too complex to apply the EMD in interactive multimedia database systems. In order to achieve the efficient retrieval, we propose a fast retrieval algorithm for the EMD. This method uses the lower bounding distance. The lower bounding distance of the EMD is simple approximation computationally.


A Method to Retrieve Telops Based on the Distance of Character Image Features between Query and Telop

Proceedings of 14th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2008)

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


A Fast Retrieval Method for the Earth Mover's Distance in Multimedia Databases


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.


Shot Boundary Detection and High-Level Feature Extraction Experiments for TRECVID 2006

Proceedings of TREC Retrieval Evaluation 2006 (TRECVID2006)


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.


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

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

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


Shot Boundary Detection and Low-Level Feature Extraction Experiments for TRECVID 2005

Proceedings of TREC Retrieval Evaluation 2005 (TRECVID2005)


Proceedings of 12th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV2006)


A Method to Retrieve Video Scenes based on the Distance of Character Image Features between Query and Telop

In this paper, a new video scene retrieval method based on telop characters is proposed. We pay attention to the kind of telop characters to be recognized for the video scene retrieval method. In order to specify the suitable scene, we have to recognize the only corresponding telop characters to the query not all characters in the video data. This method compares between each image features of telop characters and template image features of query.


Estimation of the Camera Motion based on Movement of Interest Points between Images

In this paper, we propose the efficient algorithm to estimate the camera motion by using the movement information of interest points extracted from each frame image in a video. First, some interest points are extracted from the frame image by using the Harris interest operator, and then all correspondences of each interest point between two neighboring frames are decided. Based on these movement data of interest points, camera motion parameters (zoom, pan, tilt) are calculated. In the learning phase, ``shot CM vectors'' are generated based on the three CM parameters in a shot and input to the support vector machines. In the test phase, ``window CM vectors'' are generated within each window and input to the SVM to discriminate shots which contain the camera motion.


Feedback Retrieval using the Image Knowledge Database on WWW Image Retrieval Systems

WWW image retrieval systems can retrieve the corresponding images to the query keyword from WWW, however every system cannot retrieve suitable images with high precision. In this paper, the feedback retrieval method using the image knowledge database is proposed. If the query keyword is not registered in the database, the user must select the suitable ones from image data retrieved by the conventional system, and then features of selected images are registered into the database as the supervised data.


Automatic Meta-Data Annotation of Image Region

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


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

In this paper, a new WWW image retrieval system using the image knowledge database is proposed. This system can show more suitable images by filtering retrieval results of the conventional system. If the query keyword is not registered in the database, the user must select the suitable ones from image data retrieved by the conventional system, and then features of selected images are registered into the database as the supervised data. If the query keyword is the registered one, more similar images to the supervised data in the database can be indicated in the top order. The experimental results show that the average precision of this system becomes 11.6 % better than the conventional system.


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

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


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

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

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


Image Feedback Retrieval Based on Vector Space Model Transformation


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

Proceedings of the third International Conference on Information

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


Evaluation of ETSI Advanced Front-end and Bias Removal Method on the Japanese Newspaper Article

International Speech Communication Association

本論文では，分散型音声認識システムにおける入力系の周波数特性の違いによる音声認識精度の低下を抑制するため，入力系の周波数特性を正規化する手法を提案した．提案手法は，実時間で駆動し，各フレームで最近の平均音素パラメータを探し，周波数特性を正規化するバイアスを計算する手法である．提案手法の有効性を検証するため日本語連続音声認識実験を行った結果，提案手法は入力系の周波数特性が変動しても，音声認識精度の低下は少なく，有効であることがわかった．


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

IEEE International Conference on Systems, Man and Cybernetics

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


Long-term Relevance Feedback using Simple PCA and Linear Transformation

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

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


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

7th International Conference on Spoken Language Processing

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


Development of Image Keyword Registration System for Cross-media Information Retrieval between Texts and Images

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

It is difficult to give keywords to huge image databases by human hands. In this paper, we introduce a keywords tagging support system, which has the following characteristics: (1) several users can work jointly through the network, (2) sensibility words and free descriptions can be attached in addition to system pre-defined keywords, (3) we can quickly and efficiently retrieve target images from the keyword.


Latent Semantic Indexing Based on Simple Principal Component Analysis

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


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

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


Extracting Japanese-Chinese Bilingual Word Paris with Maximum Entropy Model

Series of Information & Management Sciences

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


Dimensionality Reduction Using Non-negative Matrix Factorization for Information Retrieval

Natural Language Processing and Knowledge Engineering Mini Symposium, IEEE SYSTEMS, MAN, AND CYBERNETICS 2001 (NLPKE)

ベクトル空間情報検索モデルを用いた全文検索を行う場合，各文書を表現する索引語ベクトルがスパースになることが問題となる．そこで，本論文ではベクトル空間モデルの次元削減により情報検索精度を向上させる．次元削減には，非負行列である文書行列に対する次元削減手法として，Non-negative Matrix Factorization (NMF)を適用した．NMFは非負行列を2つの非負行列へ分解を行う手法である．提案手法は，NMFを用い非負行列である索引語文書行列を分解し，その一方の行列へ索引語文書行列を射影することにより，次元削減を行う手法である．MEDLINEを用いた情報検索実験で提案手法の有効性を示した．また，この検証結果より同一次元では従来の手法SVDを用いた手法と同等の検索精度を示すことがわかった．


Automatic Abstracting Important Sentences of Web Articles

IEEE International Conference on System, Man, and Cybernetics

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


Information Retrieval System Using Concept Projection based on PDDP Algorithm

Proceedings of PACLING2001 (Pacific Association for Computational Linguistics 2001)

In the vector space information retrieval model, text documents are often represented as high-dimensional and sparse vectors. In this paper, we propose to use the technique of dimensionality reduction using concept vectors based on PDDP algorithm as a way of solving these problems.


Dimensionality reduction of vector space model based on Simple PCA

Proc. Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies (KES)

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


Dimensionality Reduction of Vector Space Information Retrieval Model based on Non-negative Matrix Factorization

In this paper, we use Non-negative Matrix Factorization (NMF) for dimensionality reduction of the vector space model. The NMF decomposes a non-negative matrix, i.e., term-by-document matrix, into two non-negative matrices. One of the decomposed matrix can be regarded as the basis vectors. The dimensionality reduction can be performed by projecting the document vectors onto the lower dimensional space which is formed by these basis vectors.


Relevance Feedback with Support Vector Machine for Information Retrieval

Proceedings of International Conference on Computer Processing of Oriental Languages (ICCPOL)

Relevance feedback is a technique of modifying the query to improve retrieval performance based on relevance judgements from the user. We propose a relevance feedback method using support vector machine. Experimental results on Japanese test collection show that the proposed method is useful feedback method compared to the conventional feedback method.