Paper: Semi-Supervised Classification for Extracting Protein Interaction Sentences using Dependency Parsing

ACL ID D07-1024
Title Semi-Supervised Classification for Extracting Protein Interaction Sentences using Dependency Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of the paths between two protein names in the dependency parse trees of the sentences. Given two dependency trees, we define two separate similarity functions (ker- nels) based on cosine similarity and edit dis- tance among the paths between the protein names. Using these similarity functions, we investigate the performances of two classes of learning algorithms, Support Vector Ma- chines and k-nearest-neighbor, and the semi- supervised counterparts of these algorithms, transductive SVMs and harmonic functions, respectively. Significant improvement over the previous results in the literature is re- porte...