An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
C4.5: programs for machine learning
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WordNet: a lexical database for English
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A study of retrospective and on-line event detection
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Improving text categorization methods for event tracking
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IEEE Intelligent Systems
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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Neural Computation
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COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
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The stages of event extraction
ARTE '06 Proceedings of the Workshop on Annotating and Reasoning about Time and Events
A transfer approach to detecting disease reporting events in blog social media
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The Effect of Stemming on Arabic Text Classification: An Empirical Study
International Journal of Information Retrieval Research
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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 Text Summarisation. In this paper, we treat event detection as a sentence level text classification problem. Overall, we compare the performance of discriminative versus generative approaches to this task: namely, a Support Vector Machine (SVM) classifier versus a Language Modeling (LM) approach. We also investigate a rule-based method that uses handcrafted lists of `trigger' terms derived from WordNet. Two datasets are used in our experiments to test each approach on six different event types, i.e., Die, Attack, Injure, Meet, Transport and Charge-Indict. Our experimental results show that the trained SVM classifier significantly outperforms the simple rule-based system and language modeling approach on both datasets: ACE (F1 66% vs. 45% and 38%, respectively) and IBC (F1 92% vs. 88% and 74%, respectively). A detailed error analysis framework for the task is also provided which separates errors into different types: semantic, inference, continuous and trigger-less.