Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2011
Abstract
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.
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing
First Page
814
Last Page
824
City or Country
Edinburgh, Scotland, UK
Citation
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.
Available at: https://ink.library.smu.edu.sg/sis_research/1376
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons