论文名称：An approach to improve kernel-based Protein-Protein Interaction extraction by learning from large-scale network data
论文类型：期刊论文
发表刊物：METHODS
收录刊物：SCIE、PubMed
卷号：83
页面范围：44-50
ISSN号：1046-2023
关键字：Protein-Protein Interaction; Word representation; Distributed representation; Brown clusters
摘要：Protein-Protein Interaction extraction (PPIe) from biomedical literatures is an important task in biomedical text mining and has achieved desirable results on the annotated datasets. However, the traditional machine learning methods on PPIe suffer badly from vocabulary gap and data sparseness, which weakens classification performance. In this work, an approach capturing external information from the web-based data is introduced to address these problems and boost the existing methods. The approach involves three kinds of word representation techniques: distributed representation, vector clustering and Brown clusters. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora. Our code and data are available at: http://chaoslog.com/improving-kernel-based-protein-protein-interaction-extraction-by-unsupervised-word-representation-codes-and-data.html. (C) 2015 Elsevier Inc. All rights reserved.
发表时间：2015-07-15