Title: Learning event patterns from news text using bootstrapping

Authors: E. Umamaheswari; T.V. Geetha

Addresses: Department of Computer Science and Engineering, College of Engineering, Anna University, Guindy, Chennai – 600025, Tamilnadu, India ' Department of Computer Science and Engineering, College of Engineering, Anna University, Guindy, Chennai – 600025, Tamilnadu, India

Abstract: This paper presents a novel method for discovering events described in a news text. In order to determine event specific sentences, it uses a semantic graph representation to express the event semantics. The event specific patterns from the semantic graph are considered as seed examples to identify the example patterns. To extract and learn event patterns, a semi-supervised bootstrapping approach is used for event identification, which requires minimal human supervision. Our aim is to train the event identification system, using a small set of seed patterns for each event, and to find the relationship between the events using instance overlapping between the learned event patterns. Unlike other approaches, this approach uses flexible seed examples, in which it has any number of nodes/concepts and relations. It also differs in acquiring conceptually related event constraints and relations as its features, and additionally, it considers a concept hierarchy of both patterns matching and scoring. We have tested our system on a corpus consisting of 5,000 Tamil news documents, and tested the accuracy of each iteration and achieved an average accuracy of 0.574. We have also tested our approach with the existing MUC-4 dataset and achieved good precision for each event type.

Keywords: event extraction; pattern matching; event tuples; semi-supervised bootstrapping; UNL graphs; event patterns; news text; event semantics; information retrieval; news events.

DOI: 10.1504/IJICT.2015.065992

International Journal of Information and Communication Technology, 2015 Vol.7 No.1, pp.1 - 13

Received: 29 Jul 2013
Accepted: 06 Jan 2014

Published online: 17 Nov 2014 *

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