Protein-Protein Interaction Extraction System Based On SVM And Link Parse

As the quantity of biomedical literatures is increasing rapidly, various kinds of biomedical information appear in front of biomedical researchers. This brings biomedical researchers a heavy burden and makes it difficult to find needed information from these literatures rapidly. In order to improve their work efficiency, an automated facility is urgently needed to find needed information rapidly and accurately. Research on protein-protein interaction automatic extraction from biomedical literature emerges under this background. Furthermore, there is high application value in protein-protein interaction automatic extraction from biomedical literature, which can help to build protein relation network, predict protein function and design new drugs.This paper presents a protein-protein interaction extraction system for biomedical literature. This system applies the approach based on Support Vector Machine model and link parse to extract protein-protein interactions and it first uses anaphora resolution to replace the third person pronouns, then applies Conditional Random Fields model to tag protein names in biomedical text and a Link Grammar Parser to parse the link path in sentences. At last, after using feature extraction and choice of four kinds to construct feature vectors, uses Support Vector Machine model to extract complete protein-protein interactions.This paper first introduces related knowledge and works on protein-protein interaction extraction, then introduces the core approaches of system which are Statistical Learning Theory and Support Vector Machine model in detail. Later describes other approaches of system particularly, such as anaphora resolution, entity recognition, Link Grammar and Link Grammar Parser and feature choice for interaction extraction. The last part of this paper presents the implementation and the assessment of the system.