Constructing event corpus from inverted index for sentence level crime event detection and classification

Abstract

Event detection identifies interesting events from web pages and in this paper, a new approach is proposed to identify the event instances associated with an interested event type. The terms that are related to criminal activities, its co-occurrence terms and the associated sentences are considered from web documents. These sentence patterns are processed by POS tagging. Since, there is no knowledge on the sentences for the first instances, they are clustered using decision tree. Rules are formulated using pattern clusters. Priorities are assigned to the clusters based on the importance of patterns. The importance of the patterns defines the semantic relation towards event instances. Considering the priorities, weights are assigned for the rules. Artificial Neural Network (ANN) is used to classify the sentences to detect event instances based on the gained knowledge. Here ANN is used for training the weighted sentence patterns to learn the event instances of the specific event type. It is observed that the constructed rule is effective in classifying the sentences to identify event instance. The combination of these sentence patterns of the event instances are updated into the corpus. The proposed approach is encouraging when compared with other comparative approaches. © 2014 Springer International Publishing.