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"Learning An Enriched Representation From Unlabeled Data For Protein-protein Interaction Extraction." and the language of the book is English.


“Learning An Enriched Representation From Unlabeled Data For Protein-protein Interaction Extraction.” Metadata:

  • Title: ➤  Learning An Enriched Representation From Unlabeled Data For Protein-protein Interaction Extraction.
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  • Language: English

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  • Internet Archive ID: pubmed-PMC3166043

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28BMC%20Bioinformatics%29" rel="nofollow">BMC Bioinformatics</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28BMC%20Bioinformatics%29%20AND%20volume%3A%2811%29" rel="nofollow">volume 11</a>.<h2>Abstract</h2>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.

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