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2016 | OriginalPaper | Buchkapitel

Domain Adaptation with Active Learning for Named Entity Recognition

verfasst von : Huiyu Sun, Ralph Grishman, Yingchao Wang

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

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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 non-adaptive 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.

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Vorheriges Kapitel Chinese Sentiment Analysis Using Bidirectional LSTM with Word Embedding
Nächstes Kapitel An Empirical Study and Comparison for Tweet Sentiment Analysis
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Metadaten
Titel
Domain Adaptation with Active Learning for Named Entity Recognition
verfasst von
Huiyu Sun
Ralph Grishman
Yingchao Wang
Copyright-Jahr
2016
Buch
Cloud Computing and Security

Print ISBN: 978-3-319-48673-4

Electronic ISBN: 978-3-319-48674-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48674-1_54