Sentence-level event classification in unstructured texts
| Autor: | Nicola Stokes, Joe Carthy, Martina Naughton |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
business.industry
Computer science WordNet Library and Information Sciences computer.software_genre Machine learning Support vector machine Information extraction Discriminative model Question answering Artificial intelligence Language model business computer Classifier (UML) Natural language processing Sentence Information Systems |
| Zdroj: | Information Retrieval. 13:132-156 |
| ISSN: | 1573-7659 1386-4564 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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