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ASAPP 2.0: Advancing the state-of-the-art of semantic textual similarity for Portuguese

Authors Ana Alves , Hugo Gonçalo Oliveira , Ricardo Rodrigues , Rui Encarnação

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Ana Alves
  • CISUC / ISEC, Polytechnic Institute of Coimbra, Portugal
Hugo Gonçalo Oliveira
  • CISUC / Department of Informatics Engineering, {University of Coimbra, Portugal}
Ricardo Rodrigues
  • CISUC / ESEC, Polytechnic Institute of Coimbra, Portugal
Rui Encarnação
  • CISUC, University of Coimbra, Portugal

Funding

This work was partially financed by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project REMINDS - UTAP-ICDT/EEI-CTP/0022/2014.

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Ana Alves, Hugo Gonçalo Oliveira, Ricardo Rodrigues, and Rui Encarnação. ASAPP 2.0: Advancing the state-of-the-art of semantic textual similarity for Portuguese. In 7th Symposium on Languages, Applications and Technologies (SLATE 2018). Open Access Series in Informatics (OASIcs), Volume 62, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/OASIcs.SLATE.2018.12

Abstract

Semantic Textual Similarity (STS) aims at computing the proximity of meaning transmitted by two sentences. In 2016, the ASSIN shared task targeted STS in Portuguese and released training and test collections. This paper describes the development of ASAPP, a system that participated in ASSIN, but has been improved since then, and now achieves the best results in this task. ASAPP learns a STS function from a broad range of lexical, syntactic, semantic and distributional features. This paper describes the features used in the current version of ASAPP, and how they are exploited in a regression algorithm to achieve the best published results for ASSIN to date, in both European and Brazilian Portuguese.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
Keywords
  • natural language processing
  • semantic textual similarity
  • semantic relations
  • word embeddings
  • character n-grams
  • supervised machine learning

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