title: Learning an enriched representation from unlabeled data for protein-protein interaction extraction.
abstract: BACKGROUND
  Extracting protein-protein interactions from biomedical literature is an important task in biomedical text mining. Supervised machine learning methods have been used with great success in this task but they tend to suffer from data sparseness because of their restriction to obtain knowledge from limited amount of labelled data. In this work, we study the use of unlabeled biomedical texts to enhance the performance of supervised learning for this task. We use feature coupling generalization (FCG) - a recently proposed semi-supervised learning strategy - to learn an enriched representation of local contexts in sentences from 47 million unlabeled examples and investigate the performance of the new features on AIMED corpus.
  
  
  RESULTS
  The new features generated by FCG achieve a 60.1 F-score and produce significant improvement over supervised baselines. The experimental analysis shows that FCG can utilize well the sparse features which have little effect in supervised learning. The new features perform better in non-linear classifiers than linear ones. We combine the new features with local lexical features, obtaining an F-score of 63.5 on AIMED corpus, which is comparable with the current state-of-the-art results. We also find that simple Boolean lexical features derived only from local contexts are able to achieve competitive results against most syntactic feature/kernel based methods.
  
  
  CONCLUSIONS
  FCG creates a lot of opportunities for designing new features, since a lot of sparse features ignored by supervised learning can be utilized well. Interestingly, our results also demonstrate that the state-of-the art performance can be achieved without using any syntactic information in this task.
authors.affiliation: Department of Computer Science and Engineering, Dalian University of Technology, Dalian, Liaoning, 116024, China. liyanpeng.lyp@gmail.com
authors.forename: Yanpeng
authors.initials: Y
authors.lastname: Li
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Hu
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lin
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016009:Chi-Square Distribution; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D025941:Protein Interaction Mapping
references.citation: BMC Bioinformatics. 2008 Apr 11;9 Suppl 3:S6
references.pmid: 18426551
references.citation: Bioinformatics. 2004 Nov 22;20(17):3206-13
references.pmid: 15231534
references.citation: Brief Bioinform. 2005 Mar;6(1):57-71
references.pmid: 15826357
references.citation: IEEE/ACM Trans Comput Biol Bioinform. 2007 Apr-Jun;4(2):251-63
references.pmid: 17473318
references.citation: BMC Bioinformatics. 2007 Feb 09;8:50
references.pmid: 17291334
references.citation: Genome Biol. 2008;9 Suppl 2:S4
references.pmid: 18834495
references.citation: Artif Intell Med. 2005 Feb;33(2):139-55
references.pmid: 15811782
references.citation: BMC Bioinformatics. 2008 Nov 19;9 Suppl 11:S2
references.pmid: 19025688
references.citation: BMC Bioinformatics. 2009 Jul 17;10:223
references.pmid: 19615051
references.citation: Bioinformatics. 2009 Feb 1;25(3):394-400
references.pmid: 19073593
references.citation: Bioinformatics. 2007 Feb 1;23(3):365-71
references.pmid: 17142812