Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity

Vuk Batanović1 and Dragan Bojić2

  1. School of Electrical Engineering
    Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
    bv115045p@student.etf.bg.ac.rs
  2. School of Electrical Engineering
    Bulevar kralja Aleksandra 73, 11120 Belgrade, Serbia
    bojic@etf.rs

Abstract

This paper presents POST STSS, a method of determining short-text semantic similarity in which part-of-speech tags are used as indicators of the deeper syntactic information usually extracted by more advanced tools like parsers and semantic role labelers. Our model employs a part-of-speech weighting scheme and is based on a statistical bag-of-words approach. It does not require either hand-crafted knowledge bases or advanced syntactic tools, which makes it easily applicable to languages with limited natural language processing resources. By using a paraphrase recognition test, we demonstrate that our system achieves a higher accuracy than all existing statistical similarity algorithms and solutions of a more structural kind.

Key words

short-text semantic similarity, statistical similarity, corpus-based measures, part-of-speech tags, POS weighting, syntactic information, bag-of-words model, natural language processing

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS131127082B

Publication information

Volume 12, Issue 1 (January 2015)
Year of Publication: 2015
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Batanović, V., Bojić, D.: Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity. Computer Science and Information Systems, Vol. 12, No. 1, 1–31. (2015)