Word association norms, mutual information, and lexicography
Computational Linguistics
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Foundations of statistical natural language processing
Foundations of statistical natural language processing
ACM SIGKDD Explorations Newsletter
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Information extraction from biomedical text
Journal of Biomedical Informatics - Special issue: Sublanguage
I-DOCS: Distributed Agent-Assisted Knowledge Fusion for Disease Gene Discovery
ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Getting Started With SAS 9.1 Text Miner
Getting Started With SAS 9.1 Text Miner
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
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The use of text mining and natural language processing can extend into the realm of knowledge acquisition and management for biomedical applications. In this paper, we describe how we implemented natural language processing and text mining techniques on the transcribed verbal descriptions from retinal experts of biomedical disease features. The feature-attribute pairs generated were then incorporated within a user interface for a collaborative ontology development tool. This tool, IDOCS, is being used in the biomedical domain to help retinal specialists reach a consensus on a common ontology for describing age-related macular degeneration (AMD). We compare the use of traditional text mining and natural language processing techniques with that of a retinal specialist's analysis and discuss how we might integrate these techniques for future biomedical ontology and user interface development.