Learning Bayesian network using parse trees for extraction of protein-protein interaction

Extraction of protein-protein interactions from scientific papers is a relevant task in the biomedical field. Machine learning-based methods such as kernel-based represent the state-of-the-art in this task. Many efforts have focused on obtaining new types of kernels in order to employ syntactic information, such as parse trees, to extract interactions from sentences. These methods have reached the best performances on this task. Nevertheless, parse trees were not exploited by other machine learning-based methods such as Bayesian networks. The advantage of using Bayesian networks is that we can exploit the structure of the parse trees to learn the Bayesian network structure, i.e., the parse trees provide the random variables and also possible relations among them. Here we use syntactic relation as a causal dependence between variables. Hence, our proposed method learns a Bayesian network from parse trees. The evaluation was carried out over five protein-protein interaction benchmark corpora. Results show that our method is competitive in comparison with state-of-the-art methods.