Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Relation extraction systems typically rely on local lexical and syntactic features as evidence. Recent work suggests that for a given relation, there might exist certain patterns which, if present in the graph of relationships between objects, provide additional evidence for that relation. While these relational patterns can be very useful, obtaining them on a per-relation basis can be difficult. We propose template relational patterns based on the hyponymy relation. These patterns are applicable for all relations. Existing resources like WordNet are a rich and reliable source of hyponymy relationships between entities. We present techniques for making use of these template patterns for extracting relationships and incorporating them into the ontology. Our experiments show performance improvements in both tasks.