Learning what to read: Focused machine reading

Enrique Noriega-Atala, Clayton T. Morrison, Marco A. Valenzuela-Escárcega, Mihai Surdeanu

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

Abstract

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today’s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.

Original languageEnglish (US)
Title of host publicationEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages2905-2910
Number of pages6
ISBN (Electronic)9781945626838
StatePublished - Jan 1 2017
Event2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017 - Copenhagen, Denmark
Duration: Sep 9 2017Sep 11 2017

Publication series

NameEMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
CountryDenmark
CityCopenhagen
Period9/9/179/11/17

Fingerprint

Reinforcement learning
Bioinformatics
Proteins
Processing
Costs

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computational Theory and Mathematics

Cite this

Noriega-Atala, E., Morrison, C. T., Valenzuela-Escárcega, M. A., & Surdeanu, M. (2017). Learning what to read: Focused machine reading. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 2905-2910). (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings). Association for Computational Linguistics (ACL).

Learning what to read : Focused machine reading. / Noriega-Atala, Enrique; Morrison, Clayton T.; Valenzuela-Escárcega, Marco A.; Surdeanu, Mihai.

EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings. Association for Computational Linguistics (ACL), 2017. p. 2905-2910 (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings).

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

Noriega-Atala, E, Morrison, CT, Valenzuela-Escárcega, MA & Surdeanu, M 2017, Learning what to read: Focused machine reading. in EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, Association for Computational Linguistics (ACL), pp. 2905-2910, 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, 9/9/17.
Noriega-Atala E, Morrison CT, Valenzuela-Escárcega MA, Surdeanu M. Learning what to read: Focused machine reading. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings. Association for Computational Linguistics (ACL). 2017. p. 2905-2910. (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings).
Noriega-Atala, Enrique ; Morrison, Clayton T. ; Valenzuela-Escárcega, Marco A. ; Surdeanu, Mihai. / Learning what to read : Focused machine reading. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings. Association for Computational Linguistics (ACL), 2017. pp. 2905-2910 (EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings).
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