Correlation based Word Sense Disambiguation

2014 Seventh International Conference on Contemporary Computing (IC3) Pub Date : 2014-08-01 DOI:10.1109/IC3.2014.6897204

Madhavika Agarwal, Jyoti Bajpai

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引用次数: 8

Abstract

Today internet usage has seen tremendous growth. As English is the primary language, documents are mostly available in English language. In India, Hindi is the prevalent language and user wants to access data in Hindi. For the language processing we are required to get the exact sense of polysemous word interpreting the meaning in a particular context. To disambiguate the meaning of the polysemous word, the techniques used is Word Sense Disambiguation (WSD). It is a known problem in natural language processing referred as lexical semantic ambiguity. In this paper, correlation analysis of context in which the target word is used with the collocation vector of definition of target word derived from Hindi WordNet i.e. developed at IIT Bombay and the co-occurrence vector which is derived from Hindi Corpus is computed. The proposed approach uses collocation information, co-occurrence information of target word to assign weights to the different senses of ambiguous word. The evaluation is done on the 60 ambiguous words, precision obtained is 88.92%. The proposed experiment shows better efficiency.

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基于关联的词义消歧

今天，互联网的使用已经有了巨大的增长。由于英语是主要语言，所以文件大多以英语提供。在印度，印地语是流行的语言，用户希望访问印地语的数据。在语言处理中，我们需要得到多义词的确切意义，在特定的语境中解释其意义。为了消除多义词的歧义，使用的技术是词义消歧义。词汇语义歧义是自然语言处理中的一个常见问题。本文对目标词使用的语境与印度理工学院孟买分校开发的印地语WordNet中目标词定义的搭配向量进行相关性分析，并计算出印地语语料库中目标词定义的共现向量。该方法利用目标词的搭配信息、共现信息对歧义词的不同意义进行权重分配。对60个歧义词进行评价，准确率为88.92%。实验结果表明，该方法具有较高的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2014 Seventh International Conference on Contemporary Computing (IC3)

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