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 (kernels) based on cosine similarity and edit distance among the paths between the protein names. Using these similarity functions, we investigate the performances of two classes of learning algorithms, Support Vector Machines and k-nearest-neighbor, and the semisupervised counterparts of these algorithms, transductive SVMs and harmonic functions, respectively. Significant improvement over the previous results in the literature is reported as well as a new benchmark dataset is introduced. Semi-supervised algorithms perform better than their supervised version by a wide margin especially when the amount of labeled data is limited.
REF defines similarity functions based on cosine similarity and edit distance between dependency paths, and then incorporate them in SVM and KNN learning for PPI extraction.
Semi-Supervised Classification for Extracting Protein Interaction Sentences using Dependency Parsing
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science