NDLI: A Framework for Semisupervised Feature Generation and Its Applications in Biomedical Literature Mining

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A Framework for Semisupervised Feature Generation and Its Applications in Biomedical Literature Mining

Content Provider	IEEE Xplore Digital Library
Author	Yanpeng Li Xiaohua Hu Hongfei Lin Zhiahi Yang
Copyright Year	2004
Abstract	Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.
Sponsorship	IEEE Computer Society
Page Count	14
File Size	1542918
Starting Page	294
Ending Page	307
File Format	PDF
ISSN	15455963
Volume Number	8
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bioinformatics Couplings Feature extraction Computational biology Supervised learning Protein engineering Proteins text classification. Feature coupling generalization biomedical literature mining semisupervised learning named entity recognition protein-protein interaction extraction
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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