Result Aggregation for Knowledge-Intensive Multicultural Name Matching

  • Keith J. Miller
  • Mark Arehart
Conference paper

DOI: 10.1007/978-3-642-04235-5_35

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5603)
Cite this paper as:
Miller K.J., Arehart M. (2009) Result Aggregation for Knowledge-Intensive Multicultural Name Matching. In: Vetulani Z., Uszkoreit H. (eds) Human Language Technology. Challenges of the Information Society. LTC 2007. Lecture Notes in Computer Science, vol 5603. Springer, Berlin, Heidelberg

Abstract

In this paper, we describe a metasearch tool resulting from experiments in aggregating the results of different name matching algorithms on a knowledge-intensive multicultural name matching task. Three retrieval engines that match romanized names were tested on a noisy and predominantly Arabic dataset. One is based on a generic string matching algorithm; another is designed specifically for Arabic names; and the third makes use of culturally-specific matching strategies for multiple cultures. We show that even a relatively naïve method for aggregating results significantly increased effectiveness over each of the individual algorithms, resulting in nearly tripling the F-score of the worst-performing algorithm included in the aggregate, and in a 6-point improvement in F-score over the single best-performing algorithm included.

Keywords

Information Retrieval Name Matching System Combination 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Keith J. Miller
    • 1
  • Mark Arehart
    • 1
  1. 1.MITRE CorporationMcLeanUSA

Personalised recommendations