License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.SLATE.2018.12
URN: urn:nbn:de:0030-drops-92709
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9270/
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Alves, Ana ; Gonçalo Oliveira, Hugo ; Rodrigues, Ricardo ; Encarnação, Rui

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

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OASIcs-SLATE-2018-12.pdf (0.5 MB)

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.

BibTeX - Entry

@InProceedings{alves_et_al:OASIcs:2018:9270,
  author =	{Ana Alves and Hugo Gon{\c{c}}alo Oliveira and Ricardo Rodrigues and Rui Encarna{\c{c}}{\~a}o},
  title =	{{ASAPP 2.0: Advancing the state-of-the-art of semantic textual similarity for Portuguese}},
  booktitle =	{7th Symposium on Languages, Applications and Technologies  (SLATE 2018)},
  pages =	{12:1--12:17},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-072-9},
  ISSN =	{2190-6807},
  year =	{2018},
  volume =	{62},
  editor =	{Pedro Rangel Henriques and Jos{\'e} Paulo Leal and Ant{\'o}nio Menezes Leit{\~a}o and Xavier G{\'o}mez Guinovart},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9270},
  URN =		{urn:nbn:de:0030-drops-92709},
  doi =		{10.4230/OASIcs.SLATE.2018.12},
  annote =	{Keywords: natural language processing, semantic textual similarity, semantic relations, word embeddings, character n-grams, supervised machine learning}
}

Keywords: natural language processing, semantic textual similarity, semantic relations, word embeddings, character n-grams, supervised machine learning
Collection: 7th Symposium on Languages, Applications and Technologies (SLATE 2018)
Issue Date: 2018
Date of publication: 13.07.2018

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