Domain adaptation with active learning for named entity recognition

Huiyu Sun, Ralph Grishman, Yingchao Wang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

One of the dominant problems facing Named Entity Recognition is that when a system trained on one domain is applied to a different domain, a substantial drop in performance is frequently observed. In this paper, we apply active learning strategies to domain adaptation for named entity recognition systems and show that adaptive learning combining the source and target domains is more effective than nonadaptive learning directly from the target domain. Active learning aims to minimize labeling effort by selecting the most informative instances to label. We investigate several sample selection techniques such as Maximum Entropy and Smallest Margin and apply them to the ACE corpus. Our results show that the labeling cost can be reduced by over 92% without degrading the performance.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages611-622
Number of pages12
DOIs
StatePublished - Jan 1 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10040
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Active learning
  • Domain adaptation
  • Named entity recognition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Sun, H., Grishman, R., & Wang, Y. (2016). Domain adaptation with active learning for named entity recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 611-622). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10040). Springer Verlag. https://doi.org/10.1007/978-3-319-48674-1_54