Learning Recursive Patterns for Biomedical Information Extraction

  • Margherita Berardi
  • Donato Malerba
Conference paper

DOI: 10.1007/978-3-540-73847-3_15

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4455)
Cite this paper as:
Berardi M., Malerba D. (2007) Learning Recursive Patterns for Biomedical Information Extraction. In: Muggleton S., Otero R., Tamaddoni-Nezhad A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science, vol 4455. Springer, Berlin, Heidelberg

Abstract

Information in text form remains a greatly unexploited source of biological information. Information Extraction (IE) techniques are necessary to map this information into structured representations that allow facts relating domain-relevant entities to be automatically recognized. In biomedical IE tasks, extracting patterns that model implicit relations among entities is particularly important since biological systems intrinsically involve interactions among several entities. In this paper, we resort to an Inductive Logic Programming (ILP) approach for the discovery of mutual recursive patterns from text. Mutual recursion allows dependencies among entities to be explored in data and extraction models to be applied in a context-sensitive mode. In particular, IE models are discovered in form of classification rules encoding the conditions to fill a pre-defined information template. An application to a real-world dataset composed by publications selected to support biologists in the task of automatic annotation of a genomic database is reported.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Margherita Berardi
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di Informatica, Università degli Studi di Bari, via Orabona, 4 - 70126 Bari - Italy 

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