Off-Line, Handwritten Numeral Recognition by Perturbation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Kernel methods for relation extraction
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A study on convolution kernels for shallow semantic parsing
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
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 composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Shallow semantics for relation extraction
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Convolution kernels on constituent, dependency and sequential structures for relation extraction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Extracting social networks from literary fiction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Annotation scheme for social network extraction from text
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Social network extraction from texts: a thesis proposal
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Annotating social acts: authority claims and alignment moves in Wikipedia talk pages
LSM '11 Proceedings of the Workshop on Languages in Social Media
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In this paper we introduce the new task of social event extraction from text. We distinguish two broad types of social events depending on whether only one or both parties are aware of the social contact. We annotate part of Automatic Content Extraction (ACE) data, and perform experiments using Support Vector Machines with Kernel methods. We use a combination of structures derived from phrase structure trees and dependency trees. A characteristic of our events (which distinguishes them from ACE events) is that the participating entities can be spread far across the parse trees. We use syntactic and semantic insights to devise a new structure derived from dependency trees and show that this plays a role in achieving the best performing system for both social event detection and classification tasks. We also use three data sampling approaches to solve the problem of data skewness. Sampling methods improve the F1-measure for the task of relation detection by over 20% absolute over the baseline.