NDLI: Measuring Semantic Similarity in Wordnet

Content Provider	IEEE Xplore Digital Library
Author	Xiao-Ying Liu Yi-Ming Zhou Ruo-Shi Zheng
Copyright Year	2007
Description	Author affiliation: Beihang Univ., Beijing (Xiao-Ying Liu; Yi-Ming Zhou)
Abstract	Semantic similarity between words is a generic problem for many applications of computational linguistics and artificial intelligence. The difficulty of this task lies in how to find an effective way to simulate the process of human judgment of word similarity by combining and processing a number of information sources. This paper presents a novel model to measure semantic similarity between words in the WordNet, using edge-counting techniques. The fundamental idea of this model is based on the assumption that human judgment process for semantic similarity can be simulated by the ratio of common features to the total features between words. According to the experiment against a benchmark set by human similarity judgment, our measure achieves a better result. The correlation is 0.926 with average human judgment on a standard 28 word-pair dataset, which outperforms other previous reported methods.
Starting Page	3431
Ending Page	3435
File Size	717770
Page Count	5
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370741
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Length measurement Taxonomy Machine learning Cybernetics Joining processes Brain modeling Benchmark testing Solid modeling Computer science Lexical database Semantic similarity Correlation
Content Type	Text
Resource Type	Article

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