Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics

  • Tamara Polajnar
  • Mark Girolami
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

Protein-protein interaction (PPI) identification is an integral component of many biomedical research and database curation tools. Automation of this task through classification is one of the key goals of text mining (TM). However, labelled PPI corpora required to train classifiers are generally small. In order to overcome this sparsity in the training data, we propose a novel method of integrating corpora that do not contain relevance judgements. Our approach uses a semantic language model to gather word similarity from a large unlabelled corpus. This additional information is integrated into the sentence classification process using kernel transformations and has a re-weighting effect on the training features that leads to an 8% improvement in F-score over the baseline results. Furthermore, we discover that some words which are generally considered indicative of interactions are actually neutralised by this process.

Keywords

Radial Basis Function Target Word Text Mining Semantic Model Unlabelled Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tamara Polajnar
    • 1
  • Mark Girolami
    • 1
  1. 1.University of GlasgowGlasgowScotland

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