Neural joint learning for classifying wikipedia articles into fine-grained named entity types

Masatoshi Suzuki, Koji Matsuda, Satoshi Sekine, Naoaki Okazaki, Kentaro Inui

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

5 Citations (Scopus)

Abstract

This paper addresses the task of assigning finegrained NE type labels to Wikipedia articles. To address the data sparseness problem, which is salient particularly in fine-grained type classification, we introduce a multi-task learning framework where type classifiers are all jointly learned by a neural network with a hidden layer. In addition, we also propose to learn article vectors (i.e. entity embeddings) from Wikipedia's hypertext structure using a Skipgram model and incorporate them into the input feature set. To conduct large-scale practical experiments, we created a new dataset containing over 22,000 manually labeled instances. The dataset is available. The results of our experiments show that both ideas gained their own statistically significant improvement separately in classification accuracy.

Original languageEnglish
Title of host publicationProceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
EditorsJong C. Park, Jin-Woo Chung
PublisherInstitute for the Study of Language and Information
Pages535-543
Number of pages9
ISBN (Electronic)9788968174285
Publication statusPublished - 2016 Jan 1
Event30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 - Seoul, Korea, Republic of
Duration: 2016 Oct 282016 Oct 30

Publication series

NameProceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016

Other

Other30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016
CountryKorea, Republic of
CitySeoul
Period16/10/2816/10/30

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)
  • Information Systems

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  • Cite this

    Suzuki, M., Matsuda, K., Sekine, S., Okazaki, N., & Inui, K. (2016). Neural joint learning for classifying wikipedia articles into fine-grained named entity types. In J. C. Park, & J-W. Chung (Eds.), Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016 (pp. 535-543). (Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation, PACLIC 2016). Institute for the Study of Language and Information.