Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

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

A Chinese Automatic Text Summarization system for mobile devices

Universal Triple I method and its Application to Textual Emotion Polarity Recognition.

Recently, sentiment analysis for identifying positive or negative opinions from texts has received much attention. In this paper, we introduce sentiment analysis into a new field, which recognizes sentiment of relations between entities in the text. Three sentiment polarities between entities are recognized, namely positive, negative, and neutral. The difficulty in this work is that several pairs of entities may appear in the same sentence, and their sentiment polarities are determined by different related regions of the sentence. In addition, different features of words and their interactions in a related region will affect the final sentiment. It is difficult to process this using rigid rule-based methods. Therefore, we propose a machine-learning method based on statistics. In the proposed method, the model of conditional random fields (CRFs) is used to annotate the sentiment polarity between entities with the help of the syntactic dependency tree. The string of words that connects two entities in the dependency tree is used as the related region to recognize the sentiment. Experimental results and comparison with the other methods based on different principles and related regions suggest that the proposed method shows better performance and proves its validity. Moreover, the effect of different features on the CRF is word of the ith word in the sentence. We can get a CRF model using the CRF++ tool based on feature template and training corpus. After obtaining the model, sentiment of relations could be assigned automatically. The algorithm is shown in Fig. 2. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Recognizing Sentiment of Relations between Entities in Text

Recently, personal digital assistants like cellular phones are shifting to the IP terminal. The encoding-decoding process utilized for transmitting over IP networks deteriorates the quality of the speech data. This deterioration causes degradation in speech recognition performance. Acoustic model adaptations could improve recognition performance. However, the current adaptation methods usually require a large amount of adaptation data. A novel adaptation method using speech synthesis based on HMM (hidden Markov model) is proposed. This method does not require speech data for adaptation because speech data is generated by speech synthesis from the acoustic model. Experimental results on G.723.1 coded speech recognition show that the proposed method improves speech recognition performance. A relative improvement in word accuracy of approximately 2% was observed.

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Fuji Ren

Papers

A Chinese Automatic Text Summarization system for mobile devices

Universal Triple I method and its Application to Textual Emotion Polarity Recognition.

Recognizing Sentiment of Relations between Entities in Text

An acoustic model adaptation using HMM-based speech synthesis

Differently implicational α-universal triple I restriction method of (1, 2, 2) type