Named-entity techniques for terrorism event extraction and classification
Uraiwan Inyaem, Phayung Meesad, Choochart Haruechaiyasak · 2009
The aim of this paper is to study and compare several machine learning methods for implementing a Thai terrorism event extraction system. The main function of the system is to extract information related to terrorism events found in Thai news articles. The terrorism events can then be classified and presented to intelligence officers who can further analyze and predict terrorism events. This paper compares three named entity feature selection techniques provided by terrorism gazetteer, terrorism ontology and terrorism grammar rules, for entity recognition. The machine learning algorithms use for event extraction include Naiumlve Bayes (NB), K-nearest neighbor (KNN), decision tree (DTREE) and support vector machines (SVM). Each term feature is weighted by using the term frequency-inverse document frequency (TF-IDF). Finite state transduction is applied for learning feature weights. Experimental results show that the SVM algorithm with a terrorism ontology feature selection yields the best performance with 69.90% for both precision and recall.