Using Voting Approach for Event Extraction and Event-DCT, Event-Time Relation Identification

Anup Kumar Kolyal, Asif Ekbal, Sivaji Bandyopadhyay
2013 International Journal of Artificial Intelligence & Applications  
Temporal information extraction is a popular and interesting research field in the area of Natural Language Processing (NLP) applications such as summarization, question answering (QA) and information extraction. In this paper, we have reported extraction of events and identification of different temporal relations between event-time and even-document creation time (DCT) within the TimeML framework. Our long term plan is to make temporal structure that can be used in the applications like
more » ... on answering, textual entailment, summarization etc. In our approach, we propose a voted approach for (i) event extraction (ii) event -document creation time (DCT) relation identification (iii) event -time relation identification from the text under the TempEval-2 framework. The contributions of this work are two-fold; initially features are extracted from the training corpus and used to train a CRF and SVM framework. Then, the proposal of a voted approach for event extraction, event-DCT and event-time relation identification by combining the supervised classifiers such as Conditional Random Field (CRF) and Support Vector Machine (SVM). In total we generate 20 models, 10 each with CRF and SVM, by varying the available features and/or feature templates. All these 20 models are then combined together into a final system by defining appropriate voting scheme.
doi:10.5121/ijaia.2013.4106 fatcat:jt7aeki4kvfsvl7igodzuoiz4q