A fast and efficient semantic short text similarity metric

David Croft; Simon Coupland; Jethro Shell; Stephen Brown

doi:10.1109/UKCI.2013.6651309

A fast and efficient semantic short text similarity metric

David Croft, Simon Coupland, Jethro Shell, Stephen Brown

School of Computing, Electronics and Maths

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

19 Citations (Scopus)

Abstract

The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.

Original language	English
Title of host publication	13th UK Workshop on Computational Intelligence (UKCI), 2013
Publisher	IEEE
Pages	221-227
Number of pages	7
ISBN (Print)	9781479915682
DOIs	https://doi.org/10.1109/UKCI.2013.6651309
Publication status	Published - 2013
Event	13th UK Workshop on Computational Intelligence (UKCI) 2013 - University of Surrey, Guildford, United Kingdom Duration: 9 Sep 2013 → 11 Sep 2013 Conference number: 13 http://ukci2013.cs.surrey.ac.uk/

Workshop

Workshop	13th UK Workshop on Computational Intelligence (UKCI) 2013
Abbreviated title	UKCI 2013
Country	United Kingdom
City	Guildford
Period	9/09/13 → 11/09/13
Internet address	http://ukci2013.cs.surrey.ac.uk/

Keywords

Vectors
Semantics
Measurment
Natural language processing
Educational institutions
Media
Electronic mail

Access to Document

10.1109/UKCI.2013.6651309

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Cite this

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title = "A fast and efficient semantic short text similarity metric",

abstract = "The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.",

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publisher = "IEEE",

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note = "13th UK Workshop on Computational Intelligence (UKCI) 2013, UKCI 2013 ; Conference date: 09-09-2013 Through 11-09-2013",

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AU - Coupland, Simon

AU - Shell, Jethro

AU - Brown, Stephen

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N2 - The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.

AB - The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.

KW - Vectors

KW - Semantics

KW - Measurment

KW - Natural language processing

KW - Educational institutions

KW - Media

KW - Electronic mail

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DO - 10.1109/UKCI.2013.6651309

M3 - Conference proceeding

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BT - 13th UK Workshop on Computational Intelligence (UKCI), 2013

PB - IEEE

T2 - 13th UK Workshop on Computational Intelligence (UKCI) 2013

Y2 - 9 September 2013 through 11 September 2013

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