Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Text mining for semantic relations
Text mining for semantic relations
Kernel methods for relation extraction
The Journal of Machine Learning Research
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
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
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Extracting relation information from text documents by exploring various types of knowledge
Information Processing and Management: an International Journal
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Enhancing relation extraction by eliciting selectional constraint features from wikipedia
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Social relation extraction based on chinese wikipedia articles
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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In this paper, we propose a feature vector to extract semantic relations using dependency tree and parse tree. We exploit relation descriptions from infoboxes on Wikipedia documents. The features include part-of-speech, phrase label in dependency tree, and grammatical structure of phrase label, path of phrase label inherent in parse tree. In our experi ments, support vector machine and k-nearest neighbor are applied to extract relations from Wikipedia documents.