Similarity computation using semantic networks created from web-harvested data. (January 2015)

• Record Type: Journal Article
• Authors: IOSIF, ELIAS; POTAMIANOS, ALEXANDROS
• Is Part Of: Natural language engineering. Volume 21:Part 1(2015)
• Page Start: 49
• Page End: 79
• Publication Date: 2015-01
• Subjects: Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324913000144
• Languages: English
• ISSNs: 1351-3249

Abstract

<abstract abstract-type="normal"> <title>Abstract</title> <p>We investigate language-agnostic algorithms for the construction of unsupervised distributional semantic models using web-harvested corpora. Specifically, a corpus is created from web document snippets, and the relevant semantic similarity statistics are encoded in a semantic network. We propose the notion of semantic neighborhoods that are defined using co-occurrence or context similarity features. Three neighborhood-based similarity metrics are proposed, motivated by the hypotheses of attributional and maximum sense similarity. The proposed metrics are evaluated against human similarity ratings achieving state-of-the-art results.</p> </abstract>