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Supervised distributional hypernym discovery via domain adaptation

Espinosa-Anke, Luis ORCID:, Camacho-Collados, Jose, Delli Bovi, Claudio and Saggion, Horacio 2016. Supervised distributional hypernym discovery via domain adaptation. Presented at: EMNLP2016: Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA, 1-5 November 2016. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 424-435. 10.18653/v1/D16-1041

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Lexical taxonomies are graph-like hierarchical structures that provide a formal representation of knowledge. Most knowledge graphs to date rely on is-a (hypernymic) relations as the backbone of their semantic structure. In this paper, we propose a supervised distributional framework for hypernym discovery which operates at the sense level, enabling large-scale automatic acquisition of disambiguated taxonomies. By exploiting semantic regularities between hyponyms and hypernyms in embeddings spaces, and integrating a domain clustering algorithm, our model becomes sensitive to the target data. We evaluate several configurations of our approach, training with information derived from a manually created knowledge base, along with hypernymic relations obtained from Open Information Extraction systems. The integration of both sources of knowledge yields the best overall results according to both automatic and manual evaluation on ten different domains.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Association for Computational Linguistics
Date of First Compliant Deposit: 28 August 2019
Last Modified: 26 Oct 2022 07:33

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