Fuji Ren

GA, MR, FFNN, PNN and GMM based models for automatic text summarization

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

Construction of a Blog Emotion Corpus for Chinese Emotional Expression Analysis

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

A blog emotion corpus for emotional expression analysis in Chinese

Weblogs are increasingly popular modes of communication and they are frequently used as mediums for emotional expression in the ever changing online world. This work uses blogs as object and data source for Chinese emotional expression analysis. First, a textual emotional expression space model is described, and based on this model, a relatively fine-grained annotation scheme is proposed for manual annotation of an emotion corpus. In document and paragraph levels, emotion category, emotion intensity, topic word and topic sentence are annotated. In sentence level, emotion category, emotion intensity, emotional keyword and phrase, degree word, negative word, conjunction, rhetoric, punctuation, objective or subjective, and emotion polarity are annotated. Then, using this corpus, we explore these linguistic expressions that indicate emotion in Chinese, and present a detailed data analysis on them, involving mixed emotions, independent emotion, emotion transfer, and analysis on words and rhetorics for emotional expression.

Affective Information Processing and Recognizing Human Emotion

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

The Evolution of Sink Mobility Management in Wireless Sensor Networks: A Survey

Sink mobility has long been recognized as an efficient method of improving system performance in wireless sensor networks (WSNs), e.g. relieving traffic burden from a specific set of nodes. Though tremendous research efforts have been devoted to this topic during the last decades, yet little attention has been paid for the research summarization and guidance. This paper aims to fill in the blank and presents an up-to-date survey on the sink mobility issue. Its main contribution is to review mobility management schemes from an evolutionary point of view. The related schemes have been divided into four categories: uncontrollable mobility (UMM), path-restricted mobility (PRM), location-restricted mobility (LRM) and unrestricted mobility (URM). Several representative solutions are described following the proposed taxonomy. To help readers comprehend the development flow within the category, the relationship among different solutions is outlined, with detailed descriptions as well as in-depth analysis. In this way, besides some potential extensions based on current research, we are able to identify several open issues that receive little attention or remain unexplored so far.

Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing

A growing body of research suggests that affective computing has many valuable applications in enterprise systems research and e-businesses. This paper explores affective computing techniques for a vital sub-area in enterprise systems—consumer satisfaction measurement. We propose a linguistic-based emotion analysis and recognition method for measuring consumer satisfaction. Using an annotated emotion corpus (Ren-CECps), we first present a general evaluation of customer satisfaction by comparing the linguistic characteristics of emotional expressions of positive and negative attitudes. The associations in four negative emotions are further investigated. After that, we build a fine-grained emotion recognition system based on machine learning algorithms for measuring customer satisfaction; it can detect and recognize multiple emotions using customers’ words or comments. The results indicate that blended emotion recognition is able to gain rich feedback data from customers, which can provide more appropriate follow-up for customer relationship management.

Unsupervised product feature extraction for feature-oriented opinion determination

Proposes an unsupervised method of product feature extraction by using comparative domain corpora.A novel term similarity measure is introduced to evaluate the association of candidate features and domain entities.Proposes feature-oriented opinion lexicon generation for feature-oriented opinion determination. Identifying product features from reviews is the fundamental step as well as a bottleneck in feature-level sentiment analysis. This study proposes a method of unsupervised product feature extraction for feature-oriented opinion determination. The domain-specific features are extracted by measuring the similarity distance of domain vectors. A domain vector is derived based on the association values between a feature and comparative domain corpora. A novel term similarity measure (PMI-TFIDF) is introduced to evaluate the association of candidate features and domain entities. The results show that our approach of feature extraction outperforms other state-of-the-art methods, and the only external resources used are comparative domain corpora. Therefore, it is generic and unsupervised. Compared with traditional pointwise mutual information (PMI), PMI-TFIDF showed better distinction ability. We also propose feature-oriented opinion determination based on feature-opinion pair extraction and feature-oriented opinion lexicon generation. The results demonstrate the effectiveness of our proposed method and indicate that feature-oriented opinion lexicons are superior to general opinion lexicons for feature-oriented opinion determination.

An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature

The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies interaction words from unlabeled data; these interaction words are then used in relation extraction between entity pairs. Dependency parsing and phrase structure parsing are combined for relation extraction. Based on the semi-supervised KNN algorithm, we extend the proposed unsupervised approach to a semi-supervised approach by combining pattern clustering, dependency parsing and phrase structure parsing rules. We evaluated the approaches on two different tasks: (1) Protein–protein interactions extraction, and (2) Gene–suicide association extraction. The evaluation of task (1) on the benchmark dataset (AImed corpus) showed that our proposed unsupervised approach outperformed three supervised methods. The three supervised methods are rule based, SVM based, and Kernel based separately. The proposed semi-supervised approach is superior to the existing semi-supervised methods. The evaluation on gene–suicide association extraction on a smaller dataset from Genetic Association Database and a larger dataset from publicly available PubMed showed that the proposed unsupervised and semi-supervised methods achieved much higher F-scores than co-occurrence based method.

Parallel machine translation: principles and practice

Parallel machine translation (PMT) is a new machine translation paradigm that takes advantage of high-speed and large-memory computers and existing machine translation systems with different characteristics to solve the difficult machine translation problem. PMT is based on technologies of parallel computing, machine translation, and artificial intelligence. A PMT system consists of many machine translation procedures running in parallel, coordinated by a controller to dissolve various ambiguities in machine translation. We have designed and implemented a PMT system based on the above approach at a coarse parallel level. The system consists of four independent machine translation subsystems. Each subsystem is implemented using an existing machine translation technique and has its own characteristics. We present the principles and practice of PMT. We also present some results of experiments with our experimental PMT system and point out some future research on PMT.

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

This paper proposes a method to improve retrieval performance of the vector space model (VSM) in part by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the users relevance feedback information, information such as original document similarities is incorporated into the retrieval model, which is built by using a sequence of linear transformations. High-dimensional and sparse vectors are then reduced by singular value decomposition (SVD) and transformed into a low-dimensional vector space, namely the space representing the latent semantic meanings of words. The method has been tested with two test collections, the Medline collection and the Cranfield collection. In order to train the model, multiple partitions are created for each collection. Improvement of average precision of the averages over all partitions, compared with the latent semantic indexing (LSI) model, are 20.57% (Medline) and 22.23% (Cranfield) for the two training data sets, and 0.47% (Medline) and 4.78% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve user-supplied relevance information for the long term in the system in order to use it later.