文章基本信息

标题：Computation of Contextual Word Similarity Exploiting Syntactic and Semantic Structural Co-occurrences
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作者：Kazuo Hara ; Ikumi Suzuki ; Masashi Shimbo 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2013
卷号：28
期号：4
页码：379-390
DOI：10.1527/tjsai.28.379
出版社：The Japanese Society for Artificial Intelligence
摘要：We propose a new measure of semantic similarity between words in context, which exploits the syntactic/semantic structure of the context surrounding each target word. For a given pair of target words and their sentential contexts, labeled directed graphs are made from the output of a semantic parser on these sentences. Nodes in these graphs represent words in the sentences, and labeled edges represent syntactic/semantic relations between them. The similarity between the target words is then computed as the sum of the similarity of walks starting from the target words (nodes) in the two graphs. The proposed measure is tested on word sense disambiguation and paraphrase ranking tasks, and the results are promising: The proposed measure outperforms existing methods which completely ignore or do not fully exploit syntactic/semantic structural co-occurrences between a target word and its neighbors.
关键词：contextual word similarity ; word sense disambiguation ; paraphrase ; kernel method