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| 基于混合语言信息的词语搭配倾向判别方法 | |
| 引用本文： | 王素格,杨安娜.基于混合语言信息的词语搭配倾向判别方法[J].中文信息学报,2010,24(3):69-75. |
| 作者姓名： | 王素格 杨安娜 |
| 作者单位： | 1. 山西大学 数学科学学院, 山西 太原 030006; 2. 山西大学 计算智能与中文信息处理教育部重点实验室, 山西 太原 030006 |
| 基金项目： | 国家自然科学基金，教育部高等学校博士学科点专项科研基金，山西省自然科学基金，教育部科学技术研究重点项目，山西省重点实验室开放基金，太原市科技局明星专项 |
| 摘 要： | 具有较强褒贬倾向的词语搭配对于文本的情感分析具有重要的价值。该文提出了一种混合语言信息的词语搭配的倾向判别方法。该方法首先根据词语搭配六种模式的特点,确定出各模式的概率潜在语义模型,然后利用这些语义模型判别搭配的情感倾向。最后对部分包含情感词的搭配再利用规则修正其先前标注的情感倾向。基于汽车语料的实验结果表明,基于混合语言信息的词语搭配情感倾向判别方法优于单纯基于概率潜在语义模型或规则的方法。 |
| 关 键 词： | 计算机应用 中文信息处理 词语搭配 搭配模式 情感倾向判别 概率潜在语义模型 |
A Method of Collocation Orientation Identification Based on Hybrid Language Information |
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| WANG Suge,YANG Anna.A Method of Collocation Orientation Identification Based on Hybrid Language Information[J].Journal of Chinese Information Processing,2010,24(3):69-75. | |
| Authors: | WANG Suge YANG Anna |
| Affiliation: | 1. School of Mathematics Science, Shanxi University, Taiyuan, Shanxi 030006,China; 2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006,China |
| Abstract: | The collocations with strong sentiment orientation are important for the text sentiment analysis. In this paper, a method of collocation orientation identification based on hybrid language information is proposed. Firstly,according to the characteristics of six kinds of collocation patterns, the probability latent semantic models are determined for them. Then the obtained semantic models were used to identify the sentiment orientations of collocations.Lastly, for some collocations containing a sentiment word, their previous tags were modified by using some constructed rules. The experiment result in the corpus of car reviews indicates that the proposed method is superior to the method based only on probability latent semantic model or rule for collocation orientation identification. |
| Keywords: | computer application Chinese information processing collocation collocation pattern sentiment orientation identification probability latent semantic model |
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