Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions available to the research community. In this paper we present a large-scale high-quality corpus of disambiguated glosses in multiple languages, comprising sense annotations of both concepts and named entities from a unified sense inventory. Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement. As a result we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information Extraction and Sense Clustering show how two state-of-the-art approaches improve their performance by integrating our disambiguated corpus into their pipeline.

Camacho-Collados, J., Delli Bovi, C., Raganato, A., Navigli, R. (2016). A Large-Scale Multilingual Disambiguation of Glosses. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 (pp.1701-1708). European Language Resources Association (ELRA).

A Large-Scale Multilingual Disambiguation of Glosses

Raganato, A
;
2016

Abstract

Linking concepts and named entities to knowledge bases has become a crucial Natural Language Understanding task. In this respect, recent works have shown the key advantage of exploiting textual definitions in various Natural Language Processing applications. However, to date there are no reliable large-scale corpora of sense-annotated textual definitions available to the research community. In this paper we present a large-scale high-quality corpus of disambiguated glosses in multiple languages, comprising sense annotations of both concepts and named entities from a unified sense inventory. Our approach for the construction and disambiguation of the corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system; first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation, and then we combine it with a semantic similarity-based refinement. As a result we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we make it freely available at http://lcl.uniroma1.it/disambiguated-glosses. Experiments on Open Information Extraction and Sense Clustering show how two state-of-the-art approaches improve their performance by integrating our disambiguated corpus into their pipeline.
No
paper
Definitional knowledge; Entity linking; Multilingual corpus; Textual definitions; Word sense disambiguation;
English
10th International Conference on Language Resources and Evaluation, LREC 2016 - 23 May 2016 through 28 May 2016
978-295174089-1
http://www.lrec-conf.org/proceedings/lrec2016/pdf/629_Paper.pdf
Camacho-Collados, J., Delli Bovi, C., Raganato, A., Navigli, R. (2016). A Large-Scale Multilingual Disambiguation of Glosses. In Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016 (pp.1701-1708). European Language Resources Association (ELRA).
Camacho-Collados, J; Delli Bovi, C; Raganato, A; Navigli, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/361549
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