Wikipedia-based information content and semantic similarity computation
Wikipedia-based information content and semantic similarity computation
复制标题
基于维基百科的信息内容和语义相似度计算
DOI：
10.1016/j.ipm.2016.09.001
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发表时间：
2017-01-01
影响因子：
8.6
通讯作者：
Hu, Jiaojiao
中科院分区：
文献类型：
--
作者：
Jiang, Yuncheng;Bai, Wen;Hu, Jiaojiao
The Information Content (IC) of a concept is a fundamental dimension in computational linguistics. It enables a better understanding of concept's semantics. In the past, several approaches to compute IC of a concept have been proposed. However, there are some limitations such as the facts of relying on corpora availability, manual tagging, or pre-defined ontologies and fitting non-dynamic domains in the existing methods. Wikipedia provides a very large domain-independent encyclopedic repository and semantic network for computing IC of concepts with more coverage than usual ontologies. In this paper, we propose some novel methods to IC computation of a concept to solve the shortcomings of existing approaches. The presented methods focus on the IC computation of a concept (i.e., Wikipedia category) drawn from the Wikipedia category structure. We propose several new IC-based measures to compute the semantic similarity between concepts. The evaluation, based on several widely used benchmarks and a benchmark developed in ourselves, sustains the intuitions with respect to human judgments. Overall, some methods proposed in this paper have a good human correlation and constitute some effective ways of determining IC values for concepts and semantic similarity between concepts. (C) 2016 Elsevier Ltd. All rights reserved.
