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基于知网的无指导词义消歧 被引量:1

An Unsupervised Approach To Word Sense Disambiguation Based on Hownet
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摘要 词义消歧仍然是自然语言处理中一个重大的挑战,在自然语言处理的一开始,词义消歧就被认为是自然语言处理的中心任务之一。这篇文章提出了一种无导词义消歧的方法,该方法采用二阶context构造上下文向量,使用k-means算法进行聚类,最后通过计算相似度来进行词义的排歧.实验是在抽取术语的基础上进行的,在多个汉语高频多义词的两组测试中取得了平均准确率82.67%和84.55%的较好的效果。 Word sense disambiguation is still as a considered one of the most challenging problems in natural language process- ing. Ever since the field' s inception WSD has been perceived as one of the central problems in NLP. This paper presents an unsu- pervised approach which constructs context vector by means of second-order context, clustering by k-means and disambiguates by calculating the similarity. Our experiments are based on the extraction of term and average accuracy is 82.62% and 84.55% for 7 ambiguous words in open test by this method.
作者 陈浩 CHEN Hao(Department of Computer Science, Guangdong University of Finace and Economics&, Huashang College, Guangzhou 510000, China)
出处 《电脑知识与技术》 2015年第4期67-68,71,共3页 Computer Knowledge and Technology
关键词 词义消歧 HOWNET 二阶context K-MEANS聚类 word sense disambiguation Hownet second-order context clustering of k-means
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参考文献11

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二级参考文献23

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