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Multilingual Open Information Extraction: Challenges and Opportunities

1
FORMAS Research Group, Computer Science Department, Federal University of Bahia, Salvador - BA 40170-110, Brazil
2
FORMAS Research Group, Federal Institute of Rio Grande do Sul, Porto Alegre - RS 90030-040, Brazil
*
Author to whom correspondence should be addressed.
Information 2019, 10(7), 228; https://doi.org/10.3390/info10070228
Received: 1 May 2019 / Revised: 17 June 2019 / Accepted: 29 June 2019 / Published: 2 July 2019
(This article belongs to the Special Issue Natural Language Processing and Text Mining)
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Abstract

The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems. View Full-Text
Keywords: multilingual; open information extraction; parallel corpus multilingual; open information extraction; parallel corpus
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Claro, D.B.; Souza, M.; Castellã Xavier, C.; Oliveira, L. Multilingual Open Information Extraction: Challenges and Opportunities. Information 2019, 10, 228.

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