Computing Chinese Semantic Orientation Via Distributional Similarity

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Computing Chinese Semantic Orientation Via Distributional Similarity

更新时间：2025-05-08

- Computing Chinese Semantic Orientation Via Distributional Similarity
- Vol. 43, Issue 6, Pages: 33-37(2009)
- 作者机构：
  
  西北工业大学计算机学院,西安,710072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP39
- Online First：10 June 2009，
  
  Published：2009
- 稿件说明：
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Computing Chinese Semantic Orientation Via Distributional Similarity[J]. 2009, 43(6): 33-37.
DOI：

Computing Chinese Semantic Orientation Via Distributional Similarity[J]. 2009, 43(6): 33-37. DOI：

摘要

针对现有中文词汇语义倾向性计算方法存在较少考虑深层语义影响因素的问题

提出了一种利用词汇分布相似度的中文语义倾向性计算方法.该方法分2个步骤完成:①利用依存句法分析和统计工具获取词汇在语料库中的分布相似度

并综合知网(HowNet)和汉语连词特征信息优化语料库统计结果

计算中文词汇间的语义相似度; ②采用无向带权图划分的聚类方法来实现中文词汇语义倾向推断.由于获取最优聚类结果是一个NP难问题

所以采用贪心算法求解近似最优值.通过在自建的语料库上进行测试

并与利用语料库统计信息、利用HowNet等2个词汇语义倾向性计算系统进行比较

结果是所提方法的准确率达到了80%

表明在提高中文词汇语义倾向性计算的准确性方面是可行、有效的.

Abstract

An algorithm for Chinese semantic orientation calculation that uses distribution similarity is proposed to solve the problem that existing methods take less implied semantic into consideration in semantic orientation inference. The Chinese semantic orientation calculation is carried out in two steps. The first step calculates the distribution similarities using dependency grammar analysis and statistical tools. HowNet and Chinese conjunction features are introduced in semantic similarity calculation to optimize the corpus-based statistical results. The second step adopts an undirected weighted graph clustering algorithm to infer semantic orientation.Because it is an NP-hard problem to obtain the optimal clustering solution

a greedy algorithm is used to get an approximate solution. Experiments on the testing corpus show that the accuracy of the proposed algorithm is 80% and is better than both the corpus-based statistic algorithm and the HowNet-based algorithm. The results demonstrate that the proposed method is feasible and effective to improve the accuracy of Chinese semantic orientation calculation.

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references

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