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Sentence-Based Active Learning Strategies for Information Extraction

11 years 2 days ago
Sentence-Based Active Learning Strategies for Information Extraction
Given a classifier trained on relatively few training examples, active learning (AL) consists in ranking a set of unlabeled examples in terms of how informative they would be, if manually labeled, for retraining a (hopefully) better classifier. An important text learning task in which AL is potentially useful is information extraction (IE), namely, the task of identifying within a text the expressions that instantiate a given concept. We contend that, unlike in other text learning tasks, IE is unique in that it does not make sense to rank individual items (i.e., word occurrences) for annotation, and that the minimal unit of text that is presented to the annotator should be an entire sentence. In this paper we propose a range of active learning strategies for IE that are based on ranking individual sentences, and experimentally compare them on a standard dataset for named entity extraction. Keywords Information extraction, named entity recognition, active learning, selective sampling
Andrea Esuli, Diego Marcheggiani, Fabrizio Sebasti
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where IIR
Authors Andrea Esuli, Diego Marcheggiani, Fabrizio Sebastiani
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