Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Statistical models of topical content
Topic detection and tracking
Detecting adverse events for patient safety research: a review of current methodologies
Journal of Biomedical Informatics - Patient safety
Evita: a robust event recognizer for QA systems
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Linguistically motivated large-scale NLP with C&C and boxer
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
The stages of event extraction
ARTE '06 Proceedings of the Workshop on Annotating and Reasoning about Time and Events
Mining association language patterns for negative life event classification
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Sentence-level event classification in unstructured texts
Information Retrieval
Event mention detection using rough set and semantic similarity
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Extracting 5W1H event semantic elements from Chinese online news
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Event detection using lexical chain
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Journal of Biomedical Informatics
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The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Summarisation. In this paper, we treat event detection as a sentence level text classification problem. We compare the performance of two approaches to this task: a Support Vector Machine (SVM) classifier and a Language Modeling (LM) approach. We also investigate a rule based method that uses hand crafted lists of terms derived from WordNet. These terms are strongly associated with a given event type, and can be used to identify sentences describing instances of that type. We use two datasets in our experiments, and evaluate each technique on six distinct event types. Our results indicate that the SVM consistently outperform the LM technique for this task. More interestingly, we discover that the manual rule based classification system is a very powerful baseline that outperforms the SVM on three of the six event types.