Conference Proceeding Article
We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candidate slot fillers to identify meaningful template slots. We propose a generative model that incorporates distributional prior knowledge to help distribute candidates in a document into appropriate slots. Empirical results suggest that the proposed prior can bring substantial improvements to our task as compared to a K-means baseline and a Gaussian mixture model baseline. Specifically, the proposed prior has shown to be effective when coupled with discriminative features of the candidates.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing
City or Country
Edinburgh, Scotland, UK
LEUNG, Cane Wing-ki; JIANG, Jing; CHAI, Kian Ming A.; Chieu, Hai Leong; and Teow, Loo-Nin.
Unsupervised Information Extraction with Distributional Prior Knowledge. (2011). Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 814-824. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1376