Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A novel use of statistical parsing to extract information from text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Modeling commonality among related classes in relation extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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In this paper, we present a novel strategy to partly solve the data sparseness problem caused by small corpora in relation extraction by discriminatively modeling commonality among terms in each term type associated with the relation. The key idea is to use the information of terms rather than that of term pairs to extract relations. Based on this idea, terms in each term type were separately extracted from the corpora and a special function, called relation function, is used to determine whether the two terms selected from each term type have the target relation. As we can get more information of terms than that of term pairs in limited corpora, instances of the target relation we get using commonality among terms will be larger in amount and more reliable in quality. This is also proved by the experiments.