ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies

Authors

  • Luis Espinosa-Anke Universitat Pompeu Fabra
  • Horacio Saggion Universitat Pompeu Fabra
  • Francesco Ronzano Universitat Pompeu Fabra
  • Roberto Navigli Sapienza University of Rome

DOI:

https://doi.org/10.1609/aaai.v30i1.10330

Keywords:

BabelNet, Taxonomy Learning, Semantics, Domain Pertinence, Hypernym

Abstract

We introduce ExTaSem!, a novel approach for the automatic learning of lexical taxonomies from domain terminologies. First, we exploit a very large semantic network to collect housands of in-domain textual definitions. Second, we extract (hyponym, hypernym) pairs from each definition with a CRF-based algorithm trained on manually-validated data. Finally, we introduce a graph induction procedure which constructs a full-fledged taxonomy where each edge is weighted according to its domain pertinence. ExTaSem! achieves state-of-the-art results in the following taxonomy evaluation experiments: (1) Hypernym discovery, (2) Reconstructing gold standard taxonomies, and (3) Taxonomy quality according to structural measures. We release weighted taxonomies for six domains for the use and scrutiny of the community.

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Published

2016-03-05

How to Cite

Espinosa-Anke, L., Saggion, H., Ronzano, F., & Navigli, R. (2016). ExTaSem! Extending, Taxonomizing and Semantifying Domain Terminologies. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10330

Issue

Section

Technical Papers: NLP and Knowledge Representation