BioHCDP: A Hybrid Constituency-Dependency Parser for Biological NLP information extraction

Abstract

One of the key goals of biological Natural Language Processing (NLP) is the automatic information extraction from biomedical publications. Most current constituency and dependency parsers overlook the semantic relationships between the constituents comprising a sentence and may not be well suited for capturing complex long-distance dependencies. We propose in this paper a hybrid constituency-dependency parser for biological NLP information extraction called BioHCDP. BioHCDP aims at enhancing the state of the art of biological text mining by applying novel linguistic computational techniques that overcome the limitations of current constituency and dependency parsers outlined above, as follows: (1) it determines the semantic relationship between each pair of constituents in a sentence using novel semantic rules, and (2) it applies semantic relationship extraction models that represent the relationships of different patterns of usage in different contexts. BioHCDP can be used to extract various classes of data from biological texts, including protein function assignments, genetic networks, and protein-protein interactions. We compared BioHCDP experimentally with three systems. Results showed marked improvement.