Named entity recognition for novel types by transfer learning

Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, Timothy Baldwin

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

30 Citations (Scopus)

Abstract

In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model. That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.

Original languageEnglish
Title of host publicationEMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Conference Proceedings
EditorsXavier Carreras, Kevin Duh
Place of PublicationRed Hook NY USA
PublisherAssociation for Computational Linguistics (ACL)
Pages899-905
Number of pages7
ISBN (Electronic)9781945626258
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventEmpirical Methods in Natural Language Processing 2016 - Austin, United States of America
Duration: 1 Nov 20165 Nov 2016
https://www.aclweb.org/mirror/emnlp2016/
https://www.aclweb.org/anthology/volumes/D16-1/ (Proceedings)

Conference

ConferenceEmpirical Methods in Natural Language Processing 2016
Abbreviated titleEMNLP 2016
Country/TerritoryUnited States of America
CityAustin
Period1/11/165/11/16
Internet address

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