Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Experimentation as a way of life: Okapi at TREC
Information Processing and Management: an International Journal - The sixth text REtrieval conference (TREC-6)
The impact of database selection on distributed searching
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Database merging strategy based on logistic regression
Information Processing and Management: an International Journal
Cross-Language Information Retrieval
Cross-Language Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Report on CLEF-2001 Experiments: Effective Combined Query-Translation Approach
CLEF '01 Revised Papers from the Second Workshop of the Cross-Language Evaluation Forum on Evaluation of Cross-Language Information Retrieval Systems
Does pseudo-relevance feedback improve distributed information retrieval systems?
Information Processing and Management: an International Journal
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We present a new approach based on neural networks to solve the merging strategy problem for Cross-Lingual Information Retrieval (CLIR). In addition to language barrier issues in CLIR systems, how to merge a ranked list that contains documents in different languages from several text collections is also critical. We propose a merging strategy based on competitive learning to obtain a single ranking of documents merging the individual lists from the separate retrieved documents. The main contribution of the paper is to show the effectiveness of the Learning Vector Quantization (LVQ) algorithm in solving the merging problem. In order to investigate the effects of varying the number of codebook vectors, we have carried out several experiments with different values for this parameter. The results demonstrate that the LVQ algorithm is a good alternative merging strategy.