Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge-based Processing of Medical Language: A Language Engineering Approach
GWAI '92 Proceedings of the 16th German Conference on Artificial Intelligence: Advances in Artificial Intelligence
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automated processing of medical English
COLING '69 Proceedings of the 1969 conference on Computational linguistics
Towards a semantic lexicon for biological language processing: Conference Papers
Comparative and Functional Genomics
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
MPLUS: a probabilistic medical language understanding system
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Journal of Biomedical Informatics
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
A Field Theoretical Approach to Medical Natural Language Processing
IEEE Transactions on Information Technology in Biomedicine
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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 probabilistic grammar based on a training corpus consisting of concept strings and semantic classes from the Unified Medical Language System (UMLS), a comprehensive terminology resource widely used by the community. The grammar is designed to specify noun phrases only due to the nominal nature of the majority of biomedical terminological concepts. Evaluated on manually parsed clinical notes, the derived grammar achieved a recall of 0.644, precision of 0.737, and average cross-bracketing of 0.61, which demonstrated better performance than a control grammar with the semantic information removed. Error analysis revealed shortcomings that could be addressed to improve performance. The results indicated the feasibility of an approach which automatically incorporates terminology semantics in the building of an operational grammar. Although the current performance of the unsupervised solution does not adequately replace manual engineering, we believe once the performance issues are addressed, it could serve as an aide in a semi-supervised solution.