Supervised distributional hypernym discovery via domain adaptation

Espinosa-Anke, Luis; Camacho-Collados, Jose; Delli Bovi, Claudio; Saggion, Horacio

Supervised distributional hypernym discovery via domain adaptation

License

Citation

Espinosa-Anke L, Camacho-Collados J, Delli Bovi C, Saggion H. Supervised distributional hypernym discovery via domain adaptation. In: Conference on Empirical Methods in Natural Language Processing; 2016 Nov 1-5; Austin, TX. Red Hook (NY): ACL; 2016. p. 424-35.

Abstract

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.

Description

Comunicació presentada a la Conference on Empirical Methods in Natural Language Processing celebrada els dies 1 a 5 de novembre de 2016 a Austin, Texas.