AFET: Automatic fine-grained entity typing by hierarchical partial-label embedding

Xiang Ren, Wenqi He, Meng Qu, Lifu Huang, Heng Ji, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Distant supervision has been widely used in current systems of fine-grained entity typing to automatically assign categories (entity types) to entity mentions. However, the types so obtained from knowledge bases are often incorrect for the entity mention's local context. This paper proposes a novel embedding method to separately model “clean” and “noisy” mentions, and incorporates the given type hierarchy to induce loss functions. We formulate a joint optimization problem to learn embeddings for mentions and type-paths, and develop an iterative algorithm to solve the problem. Experiments on three public datasets demonstrate the effectiveness and robustness of the proposed method, with an average 15% improvement in accuracy over the next best compared method.

Original languageEnglish (US)
Title of host publicationEMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages1369-1378
Number of pages10
ISBN (Electronic)9781945626258
DOIs
StatePublished - 2016
Event2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016 - Austin, United States
Duration: Nov 1 2016Nov 5 2016

Publication series

NameEMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
CountryUnited States
CityAustin
Period11/1/1611/5/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computational Theory and Mathematics

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  • Cite this

    Ren, X., He, W., Qu, M., Huang, L., Ji, H., & Han, J. (2016). AFET: Automatic fine-grained entity typing by hierarchical partial-label embedding. In EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1369-1378). (EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d16-1144