论文名称：Improving Kernel-Based Protein-Protein Interaction Extraction by Unsupervised Word Representation
论文类型：会议论文
收录刊物：CPCI-S、SCIE、Scopus
页面范围：379-384
关键字：Protein-Protein Interaction; word representation; distributed representation; Brown clusters
摘要：As an important branch of biomedical information extraction, Protein-Protein Interaction extraction (PPIe) from biomedical literatures has been widely researched, and machine learning methods have achieved great success for this task. However, the word feature generally adopted in the existing methods suffers badly from vocabulary gap and data sparseness, weakening the classification performance. In this paper, the unsupervised word representation approach is introduced to address these problems. Three word representation methods are adopted to improve the performance of PPIe: distributed representation, vector clustering and Brown clusters representation. Experimental results show that our method outperforms the state-of-the-art methods on five publicly available corpora.
发表时间：2014-01-01