Automatic Construction of Entity Semantic Representation Model Based on Dependency Analysis

Abstract:

Article Preview

The entity semantic representation model (ESRM), which considers an entity as a set of attributes and corresponding values, is very useful for various applications. This paper proposes an approach for extracting new attributes and values from related unstructured documents. In our approach, the extraction process is formulated as the sequence labeling task. According to the predefined entity structure, the labeled data for training annotator are achieved automatically. The CRFs based annotator is trained to annotate the sentence which maybe contains the new attributes and values. And then, in terms of a decision process with scoring algorithm, new attributes and values are identified and fill into the predefined entity representation model. The experiments show that the proposed method improves the performance of extraction with a higher accuracy.

Info:

Periodical:

Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen

Pages:

1947-1951

DOI:

10.4028/www.scientific.net/AMM.121-126.1947

Citation:

X. M. Liu et al., "Automatic Construction of Entity Semantic Representation Model Based on Dependency Analysis", Applied Mechanics and Materials, Vols. 121-126, pp. 1947-1951, 2012

Online since:

October 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.