Paper Info
Title
Smoothing methods and cross-language document re-ranking
Abstract
This paper presents a report on our participation in the CLEF 2009 monolingual and bilingual ad hoc TEL@CLEF task involving three different languages: English, French and German. Language modeling was adopted as the underlying information retrieval model. While the data collection is extremely sparse, smoothing is particularly important when estimating a language model. The main purpose of the monolingual tasks is to compare different smoothing strategies and investigate the effectiveness of each alternative. This retrieval model was then used alongside a document re-ranking method based on Latent Dirichlet Allocation (LDA) which exploits the implicit structure of the documents with respect to original queries for the monolingual and bilingual tasks. Experimental results demonstrated that three smoothing strategies behave differently across testing languages while the LDA-based document re-ranking method should be considered further in order to bring significant improvement over the baseline language modeling systems in the cross-language setting.
Year
DOI
Venue
2009
10.1007/978-3-642-15754-7_6
CLEF (1)
Keywords
Field
DocType
different smoothing strategy,language model,smoothing strategy,baseline language modeling system,clef task,cross-language document re-ranking,smoothing method,underlying information retrieval model,monolingual task,different language,language modeling,retrieval model,latent dirichlet allocation,data collection
Data collection,Latent Dirichlet allocation,Information retrieval,Ranking,Computer science,Exploit,Smoothing,Natural language processing,Artificial intelligence,Clef,Language model,German
Conference
Volume
ISSN
ISBN
6241
0302-9743
3-642-15753-X
Citations 
PageRank 
References 
1
0.37
6
Authors
2
Name
Order
Citations
PageRank
Dong Zhou134225.99
Vincent Wade210614.94