Event extraction from textual data

Abstract

Many text mining techniques have been pro-posed for mining useful patterns in text documents. However, how to effectively extract and use attributes from unstructured data is still an open research issue. Event attribute extraction is a challenging research area with broad application in the field of data mining and other related field because of the importance of decision making from the hidden knowledge/patterns discovered from the textual data, for example, in crime detection: where events are extracted from an eyewitness report to concisely identify what happened during a crime. In this work, we present our approach to extracting these events based on the dependency parse tree relations of the text and its part of speech (POS). The proposed method uses a machine learning algorithm to predict events from a text. The preliminary result of the experiment run with WEKA tool shows that more than 90% of events can be predicted based on POS and the dependency relations (DepR) of a sentence.

Keywords: Events; Part of Speech; Classification; Data; Prediction

Vol. 26 No 1, June 2019