A fast and efficient semantic short text similarity metric

Date

2012-09-09

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Publisher

IEEE

Type

Conference

Peer reviewed

Yes

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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Citation

Croft, D., Coupland, S., Shell, J. and Brown, S. (2013) . A fast and efficient semantic short text similarity metric. In: 2013 13th UK workshop on computational intelligence (UKCI) pp. 221-227. IEEE.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)