Unsupervised Information Extraction with Distributional Prior Knowledge

• Main Authors: LEUNG, Cane Wing-ki, JIANG, Jing, CHAI, Kian Ming A., Chieu, Hai Leong, Teow, Loo-Nin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1376; https://ink.library.smu.edu.sg/context/sis_research/article/2375/viewcontent/D11_1075.pdf
• Institution: Singapore Management University

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.