Fine-Grained Named Entity Classification with Wikipedia Article Vectors

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

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

4 Citations (Scopus)

Abstract

This paper addresses the task of assigning multiple labels of fine-grained named entity (NE) types to Wikipedia articles. To address the sparseness of the input feature space, which is salient particularly in fine-grained type classification, we propose to learn article vectors (i.e. entity embeddings) from hypertext structure of Wikipedia using a Skip-gram 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 results of our experiments show that our idea gained statistically significant improvements in classification results.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages483-486
Number of pages4
ISBN (Electronic)9781509044702
DOIs
Publication statusPublished - 2017 Jan 12
Event2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016 - Omaha, United States
Duration: 2016 Oct 132016 Oct 16

Publication series

NameProceedings - 2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016

Other

Other2016 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2016
CountryUnited States
CityOmaha
Period16/10/1316/10/16

Keywords

  • Named entity classification
  • Wikipedia

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications

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