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
Hi-index | 0.00 |
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.