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	1. News Sentiment Classification for Chinese Document Based on Semantic Orientation
	SHI Zhenliang
	Computer Science and Technology 06 May 2011
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	Abstract:Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. In this paper, we focus on the news sentiment classification based on Chinese document-level. On the systematically analyzing the importance and difficulties of the news sentiment classification, this paper proposes an improved sentiment classification approach for Chinese document based on semantic orientation. The approach has four steps: (1) the news documents are pre-processed; (2) the sentiment words and the negative words are integrated processed; (3) the topic words and the sentiment words are integrated processed; (4) the weight is calculated based on a sentiment word dictionary and the context information. The experimental results show that our improved method can achieve better performance in Chinese document level sentiment classification.
	TO cite this article:SHI Zhenliang. News Sentiment Classification for Chinese Document Based on Semantic Orientation[OL].[ 6 May 2011] http://en.paper.edu.cn/en_releasepaper/content/4423193


	2. Ensemble Methods for Multi-Document Summarization
	WAN Xiaojun
	Computer Science and Technology 09 March 2011
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	Abstract:Ensemble methods are usually an effective way to improve system performance. The assumption is that the results of different systems are diverse and complementary to each other. This study examines the benefits of system combination for multi-document summarization. Three typical unsupervised summarization systems are developed and a few combination techniques are employed to combine the results of the individual systems. We also propose the ensemble's ensemble method to further improve the performance. Experimental results on the DUC2004 benchmark dataset demonstrate the effectiveness of the ensemble methods.
	TO cite this article:WAN Xiaojun. Ensemble Methods for Multi-Document Summarization[OL].[ 9 March 2011] http://en.paper.edu.cn/en_releasepaper/content/4415377


	3. Factors Analysis for a Computational Model of Emergent Simple Syntax
	Yu Hao,Wang Xiaojie
	Computer Science and Technology 07 November 2010
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	Abstract:This paper proposes several factors for computational models of early child language acquisition, giving a better explanation on how external language input and intrinsic parameter affect learning, comprehension and production of simple syntax. Taking a model simulating transition from one-word stage to two-word stage (O2T) as beginning, the paper gives quantitative simulation based investigations on how the language input and parameter affect the volume of system (i.e. how much is learned) and evaluation output (i.e. how well the learned can be used by the system to comprehend or produce simple syntax). Factors including contributing word, related string/concept and critical abstract factor, have been figured out to uncover underlying reasons. Contributing words bring syntax information from language input to the system; related strings/concepts relate the learned syntax to new syntax; and abstract factor is crucial for the ability of generative learning. Experiment results show that contributing word and related string/concept have much greater influence respectively on the volume of system and evaluation output, compared to other information the language input contains. Jointly with related string/concept, critical abstract factor controls evaluation output. And there exists value ranges of critical abstract factor for the occurrence of under-extension and over-extension. After that, the paper makes similar investigation on MOSAIC (i.e. a mature and widely-accepted computational model of syntax acquisition), and get similar results, which indicate some degree of generality of the factors. In the light of discrepancies between the results, the paper also gets a clearer image of MOSAIC by discussing its differences from O2T model.
	TO cite this article:Yu Hao,Wang Xiaojie. Factors Analysis for a Computational Model of Emergent Simple Syntax[OL].[ 7 November 2010] http://en.paper.edu.cn/en_releasepaper/content/4390755


	4. Sentence-level Sentiment Analysis Based on Sub-view Framework
	Xing Xinyan,Zhang Xianchao,Liu Xiaohua
	Computer Science and Technology 13 October 2010
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	Abstract:This paper presents a method for improving the performance of sentence-level sentiment analysis. Sentiment analysis is thought to require a deep understanding of the sentence structure (e.g., word order and non-local dependency). To attack this problem without the sentence parsing, we propose an approach whereby a given sentence is decomposed into a series of sub-sequences or sub-view representations. Sentence-level polarity is then determined by classifying within sub-views and fusing the obtained sub-view polarities. Two specific methods are instantiated: stacking-based maximum entropy model and hidden conditional random fields (HCRFs) based on contextual features. Extensive evaluations were carried out on two benchmark dataset, one is for sentence subjectivity classification and the other is for sentence polarity detection. Experimental results show that the performance of our proposed method is comparable to the state-of-the-art approaches.
	TO cite this article:Xing Xinyan,Zhang Xianchao,Liu Xiaohua. Sentence-level Sentiment Analysis Based on Sub-view Framework[OL].[13 October 2010] http://en.paper.edu.cn/en_releasepaper/content/4388143


	5. Sentiment Classification Using Supervised and Semi-supervised Conditional Maximum Entropy Modeling
	Qi Zhang,Xuangjing Huang,Lide Wu
	Computer Science and Technology 25 March 2009
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	Abstract:This paper presents our work on determining the sentiment polarity of sentences and articles. Feature combinations is the first novelty of our approaches. In order to explore better feature sets, we do some data analysis of the MPQA corpus and a few of experiments to evaluate combinations. Through those efforts, our approach achieves 77.0% accuracy in MPQA data set. Another novelty of this work is that Semi-supervised Conditional Maximum Entropy (SCME) modeling is used to combine labeled and unlabeled data. Experimental results show that our approach can significantly improve performance. In sentence-level, our approach achieves 78.2% accuracy in MPQA data set, the relative improvement given by semi-supervised technique is 5.2% over the supervised method.
	TO cite this article:Qi Zhang,Xuangjing Huang,Lide Wu. Sentiment Classification Using Supervised and Semi-supervised Conditional Maximum Entropy Modeling[J].International Journal of Computer Processing Of Languages ，2008年，21卷，4期，295 ~ 308


	6. The Method of Analyzing Affective Tendency in Text
	Song Guangpeng
	Computer Science and Technology 12 November 2007
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	Abstract:This paper introduces a method targeted at analyzing affective tendency of Chinese texts. First of all, Chinese texts are processed, and then affective words in texts are tagged with the affective words dictionary, and then sentence structure are analyzed. Affective values of the various elements of the sentences have different affective effect to the affective values of sentences; hence all affective elements for the affective values of sentences should be analyzed and weighted. The affective dictionary is based on psychology model, each word has two affective dimensions: activation value, pleasure value. Each word in every dimension has a corresponding value. The affective value of the text is two-dimensional. An affective tendency analyze system targeted at Chinese texts is realized, which consists of a Chinese processing engine and an affective analyzing engine. The affective tendency engine includes affective words identification function, and a rule set of sentences structure. Tests were carried out by using the affective texts.
	TO cite this article:Song Guangpeng. The Method of Analyzing Affective Tendency in Text[OL].[12 November 2007] http://en.paper.edu.cn/en_releasepaper/content/16297

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