Machine Learning
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Kernel methods for relation extraction
The Journal of Machine Learning Research
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
Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Named entity recognition using an HMM-based chunk tagger
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
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on 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
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Relation extraction and the influence of automatic named-entity recognition
ACM Transactions on Speech and Language Processing (TSLP)
Hierarchical learning strategy in semantic relation extraction
Information Processing and Management: an International Journal
Computer Speech and Language
Discriminatively Modeling Commonality of Term Types for Extracting Relation from Small Corpora
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
Mining preorder relation between knowledge units from text
Proceedings of the 2010 ACM Symposium on Applied Computing
Combining relations for information extraction from free text
ACM Transactions on Information Systems (TOIS)
Extracting hyponymy relation between Chinese terms
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Mining learning-dependency between knowledge units from text
The VLDB Journal — The International Journal on Very Large Data Bases
Incorporating lexical semantic similarity to tree kernel-based chinese relation extraction
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
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This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a linear discriminative function is determined in a top-down way using a perceptron algorithm with the lower-level weight vector derived from the upper-level weight vector. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding linear discriminative function can be determined more reliably. The upper-level discriminative function then can effectively guide the discriminative function learning in the lower-level, which otherwise might suffer from limited training data. Evaluation on the ACE RDC 2003 corpus shows that the hierarchical strategy much improves the performance by 5.6 and 5.1 in F-measure on least- and medium- frequent relations respectively. It also shows that our system outperforms the previous best-reported system by 2.7 in F-measure on the 24 subtypes using the same feature set.