Machine Learning
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
Kernel methods for relation extraction
The Journal of Machine Learning Research
Weakly-supervised relation classification for information extraction
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Social relation extraction from texts using a support-vector-machine-based dependency trigram kernel
Information Processing and Management: an International Journal
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This paper presents a supervised approach for relation extraction. We apply Support Vector Machines to detect and classify the relations in Automatic Content Extraction (ACE) corpus. We use a set of features including lexical tokens, syntactic structures, and semantic entity types for relation detection and classification problem. Besides these linguistic features, we successfully utilize the distance between two entities to improve the performance. In relation detection, we filter out the negative relation candidates using entity distance threshold. In relation classification, we use the entity distance as a feature for Support Vector Classifier. The system is evaluated in terms of recall, precision, and F-measure, and errors of the system are analyzed with proposed solution.