Deriving a probabilistic syntacto-semantic grammar for biomedicine based on domain-specific terminologies.

ABSTRACT: Biomedical natural language processing (BioNLP) is a useful technique that unlocks valuable information stored in textual data for practice and/or research. Syntactic parsing is a critical component of BioNLP applications that rely on correctly determining the sentence and phrase structure of free text. In addition to dealing with the vast amount of domain-specific terms, a robust biomedical parser needs to model the semantic grammar to obtain viable syntactic structures. With either a rule-based or corpus-based approach, the grammar engineering process requires substantial time and knowledge from experts, and does not always yield a semantically transferable grammar. To reduce the human effort and to promote semantic transferability, we propose an automated method for deriving a probabili

SUBMITTER: Fan JW 

PROVIDER: S-EPMC3172402 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature