Failure diagnosis with incomplete information in cable networks
Yun Mao, Hani Jamjoom, et al.
CoNEXT 2006
Journals and conference proceedings represent the dominant mechanisms for reporting new biomedical results. The unstructured nature of such publications makes it difficult to utilize data mining or automated knowledge discovery techniques. Annotation (or markup) of these unstructured documents represents the first step in making these documents machine-analyzable. Often, however, the use of similar (or the same) labels for different entities and the use of different labels for the same entity makes entity extraction difficult in biomedical literature. In this paper we present a system called BioAnnotator for identifying and classifying biological terms in documents. BioAnnotator uses domain-based dictionary lookup for recognizing known terms and a rule engine for discovering new terms. We explain how the system uses a biomedical dictionary to learn extraction patterns for the rule engine and how it disambiguates biological terms that belong to multiple semantic classes. © Copyright 2004 by International Business Machines Corporation.
Yun Mao, Hani Jamjoom, et al.
CoNEXT 2006
Leo Liberti, James Ostrowski
Journal of Global Optimization
Inbal Ronen, Elad Shahar, et al.
SIGIR 2009
Limin Hu
IEEE/ACM Transactions on Networking