%0 Journal Article %1 NaughtonStokesCarthy10ir %A Naughton, Martina %A Stokes, Nicola %A Carthy, Joe %D 2010 %J Information Retrieval %K v1205 springer paper ai language processing action analysis text information retrieval learn zzz.th.c4 %N 2 %P 132-156 %R 10.1007/s10791-009-9113-0 %T Sentence-Level Event Classification in Unstructured Texts %V 13 %X 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 percent vs. 45 percent and 38 percent, respectively) and IBC (F1 92 percent vs. 88 percent and 74 percent, respectively). A detailed error analysis framework for the task is also provided which separates errors into different types: semantic, inference, continuous and trigger-less.

@article{NaughtonStokesCarthy10ir, 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 percent vs. 45 percent and 38 percent, respectively) and IBC (F1 92 percent vs. 88 percent and 74 percent, respectively). A detailed error analysis framework for the task is also provided which separates errors into different types: semantic, inference, continuous and trigger-less.}, added-at = {2012-05-30T10:51:21.000+0200}, author = {Naughton, Martina and Stokes, Nicola and Carthy, Joe}, biburl = {https://www.bibsonomy.org/bibtex/2293b571a6ae13f22608314e08762f2bb/flint63}, doi = {10.1007/s10791-009-9113-0}, file = {SpringerLink:2010/NaughtonStokesCarthy10ir.pdf:PDF}, groups = {public}, interhash = {7c5e31464aba0e387f31f13245967fc3}, intrahash = {293b571a6ae13f22608314e08762f2bb}, issn = {1386-4564}, journal = {Information Retrieval}, keywords = {v1205 springer paper ai language processing action analysis text information retrieval learn zzz.th.c4}, month = {#apr#}, number = 2, pages = {132-156}, timestamp = {2018-04-16T11:44:14.000+0200}, title = {Sentence-Level Event Classification in Unstructured Texts}, username = {flint63}, volume = 13, year = 2010 }