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)


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)
Number of pages7
ISBN (Electronic)9781945626258
Publication statusPublished - 2016
Externally publishedYes
EventEmpirical Methods in Natural Language Processing 2016 - Austin, United States of America
Duration: 1 Nov 20165 Nov 2016 (Proceedings)


ConferenceEmpirical Methods in Natural Language Processing 2016
Abbreviated titleEMNLP 2016
Country/TerritoryUnited States of America
Internet address

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