Preprint Article Version 1 This version is not peer-reviewed

Multilingual Open Information Extraction: Challenges and Opportunities

Version 1 : Received: 1 May 2019 / Approved: 6 May 2019 / Online: 6 May 2019 (06:14:07 CEST)

How to cite: Claro, D.B.; Souza, M.; Castellã Xavier, C.; Oliveira, L. Multilingual Open Information Extraction: Challenges and Opportunities. Preprints 2019, 2019050029 (doi: 10.20944/preprints201905.0029.v1). Claro, D.B.; Souza, M.; Castellã Xavier, C.; Oliveira, L. Multilingual Open Information Extraction: Challenges and Opportunities. Preprints 2019, 2019050029 (doi: 10.20944/preprints201905.0029.v1).

Abstract

The number of documents published on the Web other languages than English grows every year. As a consequence, it increases the necessity of extracting useful information from different languages, pointing out the importance of researching Open Information Extraction (OIE) techniques. Different OIE methods have been dealing with features from a unique language. On the other hand, few approaches tackle multilingual aspects. In such approaches, multilingual is only treated as an extraction method, which results in low precision due to the use of general rules. Multilingual methods have been applied to a vast amount of 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 state that a multilingual approach can enhance OIE methods, being ideal to evaluate and compare OIE systems, and as a consequence, to applying it to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase the acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning the 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.

Subject Areas

multilingual; open information extraction; parallel corpus

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