Accession Number:

AD1046852

Title:

Collectively Representing Semi-Structured Data from the Web

Descriptive Note:

Conference Paper

Corporate Author:

Carnegie Mellon University Pittsburgh United States

Personal Author(s):

• Dalvi,Bhavana
• Cohen,William W.
• Callan,Jamie

Report Date:

2012-06-07

Pagination or Media Count:

7.0

Abstract:

In this paper, we propose a single low dimensional representation of a large collection of table and hyponym data, and show that with a small number of primitive operations, this representation can be used effectively for many purposes. Specifically we consider queries like set expansion, class prediction etc. We evaluate our methods on publicly available semi-structured datasets from the Web.

Descriptors:

• DATA STRUCTURES
• internet
• feature extraction
• graphs
• algorithms
• clustering
• SEMI SUPERVISED LEARNING
• probability
• DATA SET

Subject Categories:

• Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE