Sentence-level event classification in unstructured texts |
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| Authors: | M Naughton N Stokes J Carthy |
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| Institution: | (1) School of Computer Science and Informatics, University College Dublin, Dublin, Ireland |
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| Abstract: | 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. |
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