文章基本信息

标题：Active Learning with Subsequence Sampling Strategy for Sequence Labeling Tasks
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作者：Dittaya Wanvarie ; Hiroya Takamura ; Manabu Okumura 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2011
卷号：6
期号：3
页码：680-700
DOI：10.11185/imt.6.680
出版社：Information and Media Technologies Editorial Board
摘要：We propose an active learning framework for sequence labeling tasks. In each iteration, a set of subsequences are selected and manually labeled, while the other parts of sequences are left unannotated. The learning will stop automatically when the training data between consecutive iterations does not significantly change. We evaluate the proposed framework on chunking and named entity recognition data provided by CoNLL. Experimental results show that we succeed in obtaining the supervised F ₁ only with 6.98%, and 7.01% of tokens being annotated, respectively.
关键词：Active Learning;Sequence Labeling;Partial Annotation