A Wikipedia-Based Multilingual Retrieval Model

A Wikipedia-Based Multilingual Retrieval Model

Cached

Download Links

[www.uni-weimar.de]

Other Repositories/Bibliography

DBLP

by Martin Potthast , Benno Stein , Maik Anderka

Citations:

12 - 1 self

Versions

Version 0

Version History

Metadata Version 0

Datum	Value	Source
TITLE	A Wikipedia-Based Multilingual Retrieval Model	SVM HeaderParse 0.2
AUTHOR NAME	Martin Potthast	SVM HeaderParse 0.2
AUTHOR ADDR	Bauhaus University Weimar, Faculty of Media, 99421 Weimar, Germany	SVM HeaderParse 0.2
AUTHOR NAME	Benno Stein	SVM HeaderParse 0.2
AUTHOR ADDR	Bauhaus University Weimar, Faculty of Media, 99421 Weimar, Germany	SVM HeaderParse 0.2
AUTHOR NAME	Maik Anderka	SVM HeaderParse 0.2
AUTHOR ADDR	Bauhaus University Weimar, Faculty of Media, 99421 Weimar, Germany	SVM HeaderParse 0.2
ABSTRACT	Abstract. This paper introduces CL-ESA, a new multilingual retrieval model for the analysis of cross-language similarity. The retrieval model exploits the multilingual alignment of Wikipedia: given a document d written in language L we construct a concept vector d for d, where each dimension i in d quantifies the similarity of d with respect to a document d ∗ i chosen from the “L-subset ” of Wikipedia. Likewise, for a second document d ′ written in language L ′ , L � = L ′, we construct a concept vector d ′ , using from the L ′-subset of the Wikipedia the topic-aligned counterparts d ′∗ i of our previously chosen documents. Since the two concept vectors d and d ′ are collection-relative representations of d and d ′ they are language-independent. I. e., their similarity can directly be computed with the cosine similarity measure, for instance. We present results of an extensive analysis that demonstrates the power of this new retrieval model: for a query document d the topically most similar documents from a corpus in another language are properly ranked. Salient property of the new retrieval model is its robustness with respect to both the size and the quality of the index document collection. 1	SVM HeaderParse 0.2
CITATIONS	15 found	ParsCit 1.0