Sentence-based active learning strategies for information extraction

Andrea Esuli, Diego Marcheggiani, Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


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. Copyright owned by the authors.

Original languageEnglish
Title of host publicationCEUR Workshop Proceedings
Number of pages5
Publication statusPublished - 2010
Externally publishedYes
Event1st Italian Information Retrieval Workshop, IIR 2010 - Padua, Italy
Duration: 27 Jan 201028 Jan 2010


Other1st Italian Information Retrieval Workshop, IIR 2010



  • Active learning
  • Information extraction
  • Named entity recognition
  • Selective sampling

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Esuli, A., Marcheggiani, D., & Sebastiani, F. (2010). Sentence-based active learning strategies for information extraction. In CEUR Workshop Proceedings (Vol. 560, pp. 41-45). CEUR-WS.