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Universal Dependencies for Multilingual Open Information Extraction

Authors Massinissa Atmani, Mathieu Lafourcade

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Massinissa Atmani
  • LIRMM, University of Montpellier, 860 rue de St Priest, 34095 Montpellier, France
  • Amaris Research Unit, 25 boulevard Eugène Deruelle, 69003 Lyon, France
  • massinissa.atmani@etu.umontpellier.fr
Mathieu Lafourcade
  • LIRMM, University of Montpellier, 860 rue de St Priest 34095 Montpellier, France

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Massinissa Atmani and Mathieu Lafourcade. Universal Dependencies for Multilingual Open Information Extraction. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 24:1-24:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.24

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).

Subject Classification

ACM Subject Classification
  • Computing methodologies → Information extraction
Keywords
  • Natural Language Processing
  • Information Extraction
  • Machine Learning

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References

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