Business Event Extraction System Based on SSVM.
Sung-Ho Shin, Young Min Kim, Choong Nyoung Seon, Seunggyun Hong, Sa Kwang Song, Hanmin Jung · International Semantic Technology Conference · 2014
Information extraction from unstructured text data has been used essentially to provide new insights by collecting, storing, and analyzing text data in textual analysis. The research on event extraction has been recently getting more attention in information extraction area since lots of events happens and significantly affect our societies and countries. Related studies on event extraction use rules for identifying and extracting events from texts so far. However, rule based approaches have a limitation in terms of accuracy and rule construction. In this paper, we present an event extraction system which takes advantage of the machine learning method, especially SSVM. The system extracts event triggers and predefined event arguments, while existing rule based systems extract unknown event arguments. Ours provides 60.23 F1 score, which is higher than that of previous researches, in which rule based event extractions were performed. Even though rule-based and machine learning-based approaches cannot be compared against each other completely fairly, what is clear is that for the task in which event arguments are defined in advance, applying machine learning method can make better results.