License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.LDK.2021.24
URN: urn:nbn:de:0030-drops-145600
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14560/
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Atmani, Massinissa ; Lafourcade, Mathieu

Universal Dependencies for Multilingual Open Information Extraction

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OASIcs-LDK-2021-24.pdf (0.7 MB)

Abstract

In this paper, we present our approach for Multilingual Open Information Extraction. Our sequence labeling based approach builds only on Universal Dependency representation to capture OpenIE’s regularities and to perform Cross-lingual Multilingual OpenIE. We propose a new two-stage pipeline model for sequence labeling, that first identifies all the arguments of the relation and only then classifies them according to their most likely label. This paper also introduces a new benchmark evaluation for French. Experimental Evaluation shows that our approach achieves the best results in the available Benchmarks (English, French, Spanish and Portuguese).

BibTeX - Entry

@InProceedings{atmani_et_al:OASIcs.LDK.2021.24,
  author =	{Atmani, Massinissa and Lafourcade, Mathieu},
  title =	{{Universal Dependencies for Multilingual Open Information Extraction}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{24:1--24:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14560},
  URN =		{urn:nbn:de:0030-drops-145600},
  doi =		{10.4230/OASIcs.LDK.2021.24},
  annote =	{Keywords: Natural Language Processing, Information Extraction, Machine Learning}
}

Keywords: Natural Language Processing, Information Extraction, Machine Learning
Collection: 3rd Conference on Language, Data and Knowledge (LDK 2021)
Issue Date: 2021
Date of publication: 30.08.2021

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