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From Lexical to Semantic Features in Paraphrase Identification

Authors Pedro Fialho , Luísa Coheur , Paulo Quaresma

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Author Details

Pedro Fialho
  • INESC-ID, Lisboa, Portugal
  • Universidade de Évora, Portugal
Luísa Coheur
  • INESC-ID, Lisboa, Portugal
  • Instituto Superior Tecnico, Universidade de Lisboa, Portugal
Paulo Quaresma
  • INESC-ID, Lisboa, Portugal
  • Universidade de Évora, Portugal

Acknowledgements

This work was supported by national funds through Fundação para a Ciência e Tecnologia (FCT) with reference UID/CEC/50021/2019, through the international project RAGE with reference H2020-ICT-2014-1/644187 and by FCT’s INCoDe 2030 initiative, in the scope of the demonstration project AIA, "Apoio Inteligente a empreendedores (chatbots)", which also supports the scholarship of Pedro Fialho.

Cite AsGet BibTex

Pedro Fialho, Luísa Coheur, and Paulo Quaresma. From Lexical to Semantic Features in Paraphrase Identification. In 8th Symposium on Languages, Applications and Technologies (SLATE 2019). Open Access Series in Informatics (OASIcs), Volume 74, pp. 9:1-9:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.SLATE.2019.9

Abstract

The task of paraphrase identification has been applied to diverse scenarios in Natural Language Processing, such as Machine Translation, summarization, or plagiarism detection. In this paper we present a comparative study on the performance of lexical, syntactic and semantic features in the task of paraphrase identification in the Microsoft Research Paraphrase Corpus. In our experiments, semantic features do not represent a gain in results, and syntactic features lead to the best results, but only if combined with lexical features.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
  • Theory of computation → Support vector machines
  • Information systems → Near-duplicate and plagiarism detection
Keywords
  • paraphrase identification
  • lexical features
  • syntactic features
  • semantic features

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