Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on 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
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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
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ACL '04 Proceedings of the 42nd 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
Exploring various knowledge in relation extraction
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
An Empirical Research on Extracting Relations from Wikipedia Text
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
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BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Tree kernel-based semantic relation extraction with rich syntactic and semantic information
Information Sciences: an International Journal
Clustering-based stratified seed sampling for semi-supervised relation classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Developing Position Structure-Based Framework for Chinese Entity Relation Extraction
ACM Transactions on Asian Language Information Processing (TALIP)
ACM Transactions on Asian Language Information Processing (TALIP)
Extraction of semantic relation based on feature vector from wikipedia
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
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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Extracting semantic relationships between entities from text documents is challenging in information extraction and important for deep information processing and management. This paper investigates the incorporation of diverse lexical, syntactic and semantic knowledge in feature-based relation extraction using support vector machines. Our study illustrates that the base phrase chunking information is very effective for relation extraction and contributes to most of the performance improvement from syntactic aspect while current commonly used features from full parsing give limited further enhancement. This suggests that most of useful information in full parse trees for relation extraction is shallow and can be captured by chunking. This indicates that a cheap and robust solution in relation extraction can be achieved without decreasing too much in performance. We also demonstrate how semantic information such as WordNet, can be used in feature-based relation extraction to further improve the performance. Evaluation on the ACE benchmark corpora shows that effective incorporation of diverse features enables our system outperform previously best-reported systems. It also shows that our feature-based system significantly outperforms tree kernel-based systems. This suggests that current tree kernels fail to effectively explore structured syntactic information in relation extraction.