Learning dictionaries for information extraction by multi-level bootstrapping
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
Evaluation of DEFINDER: a system to mine definitions from consumer-oriented medical text
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
MindNet: acquiring and structuring semantic information from text
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Answering what-is questions by Virtual Annotation
HLT '01 Proceedings of the first international conference on Human language technology research
Extending metadata definitions by automatically extracting and organizing glossary definitions
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
Soft pattern matching models for definitional question answering
ACM Transactions on Information Systems (TOIS)
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An obstacle to understanding results across heterogeneous databases is the ability to determine conceptual connections between differing terminologies. In this paper, we present the two step approach which we have used to build a terminological database in order to address this issue. First we automatically built a heterogeneous collection of terms and definitions from two types of dynamic sources: 1) glossaries automatically identified from 147 government web sites and 2) definitions extracted from 600 unstructured articles. After storing terms and their definitions, we semantically analyzed the definitions to store the terminological knowledge in a relational database. Currently the database contains 12,780 definitions of 8,431 terms.