Article

CrossOIE: Cross-Lingual Classifier for Open Information Extraction

Authors:

Bruno Souza Cabral,

Rafael Glauber,

Daniela Barreiro ClaroAuthors Info & Claims

Computational Processing of the Portuguese Language: 14th International Conference, PROPOR 2020, Evora, Portugal, March 2–4, 2020, Proceedings

Pages 368 - 378

https://doi.org/10.1007/978-3-030-41505-1_35

Published: 02 March 2020 Publication History

Abstract

Open information extraction (Open IE) is the task of extracting open-domain assertions from natural language sentences. Considering the low availability of datasets and tools for this task in languages other than English, recently it has been proposed that multilingual resources can be used to improve Open IE methods for different languages. In this work, we present the CrossOIE, a multilingual publicly available relation tuple validity classifier that scores Open IE systems’ extractions based on their estimated quality and can be used to improve Open IE systems and assist in the creation of Open IE benchmarks for different languages. Experiments show that our model trained using a small corpus in English, Spanish, and Portuguese can trade recall performance for up to 27% improvement in precision. This result was also archived in a zero-shot scenario, demonstrating a successful knowledge transfer across the languages.

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Cited By

Cabral BSouza MClaro D(2022)PortNOIE: A Neural Framework for Open Information Extraction for the Portuguese LanguageComputational Processing of the Portuguese Language10.1007/978-3-030-98305-5_23(243-255)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-030-98305-5_23
Liang YQin Y(2020)Cross-Lingual Public Opinion Tracing Based on Blockchain TechnologyWeb Information Systems and Applications10.1007/978-3-030-60029-7_54(607-617)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_54

Index Terms

CrossOIE: Cross-Lingual Classifier for Open Information Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Guide Proceedings

Computational Processing of the Portuguese Language: 14th International Conference, PROPOR 2020, Evora, Portugal, March 2–4, 2020, Proceedings

Mar 2020

431 pages

ISBN:978-3-030-41504-4

DOI:10.1007/978-3-030-41505-1

Editors:
Paulo Quaresma
University of Évora, Evora, Portugal
,
Renata Vieira
University of Évora, Evora, Portugal
,
Sandra Aluísio
University of São Paulo, São Carlos, Brazil
,
Helena Moniz
University of Lisbon, Lisbon, Portugal
,
Fernando Batista
INESC-ID/ISCTE-IUL, Lisbon, Portugal
,
Teresa Gonçalves
University of Évora, Evora, Portugal

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 02 March 2020

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Cited By

Cabral BSouza MClaro D(2022)PortNOIE: A Neural Framework for Open Information Extraction for the Portuguese LanguageComputational Processing of the Portuguese Language10.1007/978-3-030-98305-5_23(243-255)Online publication date: 21-Mar-2022
https://dl.acm.org/doi/10.1007/978-3-030-98305-5_23
Liang YQin Y(2020)Cross-Lingual Public Opinion Tracing Based on Blockchain TechnologyWeb Information Systems and Applications10.1007/978-3-030-60029-7_54(607-617)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_54

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