Home // IMMM 2011, The First International Conference on Advances in Information Mining and Management // View article
Authors:
Hao Li
Heng Ji
Hongbo Deng
Jiawei Han
Keywords: Event Extraction; Background Information Network; Biased Propagation based Topic Modeling.
Abstract:
In this paper, we describe a novel approach of biased propagation based topic modeling to exploit global background knowledge for enhancing both the quality and portability of event extraction on unstructured data. The distributions of event triggers and arguments in topically related documents are much more focused than those in a heterogeneous corpus. Based on this intuition, we apply topic modeling to automatically select training documents for annotation, and demonstrate it can significantly reduce annotation cost in order to achieve comparable performance for two different languages and two different genres. In addition, we conduct cross-document inference within each topic cluster and show that our approach advances state-of-the-art.
Pages: 23 to 30
Copyright: Copyright (c) IARIA, 2011
Publication date: October 23, 2011
Published in: conference
ISSN: 2326-9332
ISBN: 978-1-61208-162-5
Location: Barcelona, Spain
Dates: from October 23, 2011 to October 29, 2011