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.