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Effective reranking for extracting protein-protein interactions from biomedical literature

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2007). Effective reranking for extracting protein-protein interactions from biomedical literature. In: Sixth International Conference on Bioinformatics (InCoB 2007), 27-31 Aug 2007, Hong Kong.

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A semantic parser based on the hidden vector state (HVS) model has been proposed for extracting protein-protein interactions. The HVS model is an extension of the basic discrete hidden Markov model, in which context is encoded as a stack-oriented state vector and state transitions are factored into a stack shift operation followed by the push of a new preterminal category label. In this paper, we investigate three different models, log-linear regression (LLR), neural networks (NNs) and support vector machines (SVMs), to rerank parses generated by the HVS model for protein-protein interactions extraction. Features used for reranking are manually defined which include the parse information, the structure information, and the complexity information. The experimental results show that reranking can indeed improve the performance of protein-protein interactions extraction, and reranking based on SVM gives more stable performance than LLR and NN.

Item Type: Conference or Workshop Item
Copyright Holders: The Authors
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
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Item ID: 28560
Depositing User: Kay Dave
Date Deposited: 18 May 2011 15:44
Last Modified: 07 Dec 2018 23:34
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