NDLI: Exploiting synonymy to measure semantic similarity of sentences

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Exploiting synonymy to measure semantic similarity of sentences

Content Provider	ACM Digital Library
Author	Shin, Youhyun Kim, Hyuntak Ahn, Yeonchan Lee, Sang-goo
Abstract	The importance of semantic similarity measures between sentences is increasingly growing in text mining, text clustering, and question answering. Many studies have focused on finding exact term matching to predict sentence similarity. In this paper, we present a method for measuring sematic similarity of sentences based on constructed synonymy graph to avoid considering just exactly matching terms. When we construct graph which has terms as nodes and synonymy relation as edges, we use WordNet and part-of-speech to exploit synonyms. We assume synonym of a synonym is also similar; it takes advantage of the fact friend of a friend is likely to be a friend as well in real-world. With this concept, similarity between words is estimated by exploiting the minimum number of synonym chains between two nodes. The proposed algorithm calculates similarity of two sentences by summing all the similarities between selected words in sentences. Evaluation is conducted on two different data sets, Microsoft Research paraphrase corpus, and Yelp review dataset. Experimental evidences show that 1) the proposed method is more accurate compared to existing sentence similarity measures and 2) using real-world dataset like Yelp reveals that the proposed method has chance to be applied to recommendation.
Starting Page	1
Ending Page	4
Page Count	4
File Format	PDF
ISBN	9781450333771
DOI	10.1145/2701126.2701219
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information retrieval Semantic similarity Sentence similarity
Content Type	Text
Resource Type	Article

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