Term disambiguation techniques based on target document collection for cross-language information retrieval: An empirical comparison of performance between techniques

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Dictionary-based query translation for cross-language information retrieval often yields various translation candidates having different meanings for a source term in the query. This paper examines methods for solving the ambiguity of translations based on only the target document collections. First, we discuss two kinds of disambiguation technique: (1) one is a method using term co-occurrence statistics in the collection, and (2) a technique based on pseudo-relevance feedback. Next, these techniques are empirically compared using the CLEF 2003 test collection for German to Italian bilingual searches, which are executed by using English language as a pivot. The experiments showed that a variation of term co-occurrence based techniques, in which the best sequence algorithm for selecting translations is used with the Cosine coefficient, is dominant, and that the PRF method shows comparable high search performance, although statistical tests did not sufficiently support these conclusions. Furthermore, we repeat the same experiments for the case of French to Italian (pivot) and English to Italian (non-pivot) searches on the same CLEF 2003 test collection in order to verity our findings. Again, similar results were observed except that the Dice coefficient outperforms slightly the Cosine coefficient in the case of disambiguation based on term co-occurrence for English to Italian searches.

Original languageEnglish
Pages (from-to)103-120
Number of pages18
JournalInformation Processing and Management
Volume43
Issue number1
DOIs
Publication statusPublished - 2007 Jan 1

Keywords

  • Cross-language information retrieval
  • Dictionary-based query translation
  • Pseudo-relevance feedback
  • Statistical comparison
  • Term co-occurrence statistics
  • Term disambiguation

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences

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