Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Enhancing performance of protein and gene name recognizers with filtering and integration strategies
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Improving the performance of dictionary-based approaches in protein name recognition
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A lexical metaschema for the UMLS semantic network
Artificial Intelligence in Medicine
Extraction of regulatory gene/protein networks from Medline
Bioinformatics
Terminology model discovery using natural language processing and visualization techniques
Journal of Biomedical Informatics
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
A prototype symbolic model of canonical functional neuroanatomy of the motor system
Journal of Biomedical Informatics
Guest Editorial: Current issues in biomedical text mining and natural language processing
Journal of Biomedical Informatics
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With a rapid progress in the field, a great many fMRI studies are published every year, to the extent that it is now becoming difficult for researchers to keep up with the literature, since reading papers is extremely time-consuming and labor-intensive. Thus, automatic information extraction has become an important issue. In this study, we used the Unified Medical Language System (UMLS) to construct a hierarchical concept-based dictionary of brain functions. To the best of our knowledge, this is the first generalized dictionary of this kind. We also developed an information extraction system for recognizing, mapping and classifying terms relevant to human brain study. The precision and recall of our system was on a par with that of human experts in term recognition, term mapping and term classification. Our approach presented in this paper presents an alternative to the more laborious, manual entry approach to information extraction.