Sentence-level event classification in unstructured texts

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. 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 `trigger' terms derived from WordNet. We use two datasets in our experiments and test each approach using six different event types, i.e, Die, Attack, Injure, Meet, Transport and Charge-Indict. Our experimental results indicate that although the trained SVM classifier consistently outperforms the language modeling approach, our rule-based system marginally outperforms the trained SVM classifier on three of our six event types. We also observe that overall performance is greatly affected by the type of corpus...

• Type: Journal
• Year: 2010
• Where: IR
• Authors: Martina Naughton, Nicola Stokes, Joe Carthy
• Keywords: Event Type, Heterogeneous Dataset, IR 2010, Natural Language Processing, Trained Svm