PERSIVAL, a system for personalized search and summarization over multimedia healthcare information
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Using contextual spelling correction to improve retrieval effectiveness in degraded text collections
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Building a terminological database from heterogeneous definitional sources
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
A supervised learning approach to search of definitions
Journal of Computer Science and Technology - Special section on China AVS standard
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ACM Transactions on Information Systems (TOIS)
Text analysis for ontology and terminology engineering
Applied Ontology
Text analysis for ontology and terminology engineering
Applied Ontology
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In this paper we present DEFINDER, a rule-based system that mines cons umer-oriented full text articles in order to extract definitions and the terms they define. This research is part of Digital Library Project at Columbia University, entitled PERSIVAL (PErsonalized Retrieval and Summarization of Image, Video and Language resources) [5]. One goal of the project is to present information to patients in language they can understand. A key component of this stage is to provide accurate and readable lay definitions for technical terms, which may be present in articles of intermediate complexity. The focus of this short paper is on quantitative and qualitative evaluation of the DEFINDER system [3]. Our basis for comparison was definitions from Unified Medical Language System (UMLS), On-line Medical Dictionary (OMD) and Glossary of Popular and Technical Medical Terms (GPTMT). Quantitative evaluations show that DEFINDER obtained 87% precision and 75% recall and reveal the incompleteness of existing resources and the ability of DEFINDER to address gaps. Qualitative evaluation shows that the definitions extracted by our system are ranked higher in terms of user-based criteria of usability and readability than definitions from on-line specialized dictionaries. Thus the output of DEFINDER can be used to enhance existing specialized dictionaries, and also as a key feature in summarizing technical articles for non-specialist users.