Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Word association norms, mutual information, and lexicography
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data
Journal of Biomedical Informatics
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Unsupervised gene/protein named entity normalization using automatically extracted dictionaries
ISMB '05 Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics
Representing sentence structure in hidden Markov models for information extraction
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence in Medicine
Design and development of a concept-based multi-document summarization system for research abstracts
Journal of Information Science
Assessing clinical trial eligibility with logic expression queries
Data & Knowledge Engineering
Syntactic dependency based heuristics for biological event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Entity Resolution in Texts Using Statistical Learning and Ontologies
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Detecting hedge cues and their scope in biomedical text with conditional random fields
Journal of Biomedical Informatics
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The task of mining relations from collections of documents is usually approached in two different ways. One type of systems do relation extraction from individual sentences, followed by an aggregation of the results over the entire collection. Other systems follow an entirely different approach, in which co-occurrence counts are used to determine whether the mentioning together of two entities is due to more than simple chance. We show that increased extraction performance can be obtained by combining the two approaches into an integrated relation extraction model.