Information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Stemming algorithms: a case study for detailed evaluation
Journal of the American Society for Information Science - Special issue: evaluation of information retrieval systems
Viewing stemming as recall enhancement
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Corpus-based stemming using cooccurrence of word variants
ACM Transactions on Information Systems (TOIS)
A stemming procedure and stopword list for general French corpora
Journal of the American Society for Information Science
Automatic Language-Specific Stemming in Information Retrieval
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
West Group at CLEF 2000: Non-english Monolingual Retrieval
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Current research issues and trends in non-English Web searching
Information Retrieval
Query Expansion Based on Query Log and Small World Characteristic
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
Mining tag similarity in folksonomies
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Natural language technology and query expansion: issues, state-of-the-art and perspectives
Journal of Intelligent Information Systems
Translation techniques in cross-language information retrieval
ACM Computing Surveys (CSUR)
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Information retrieval systems (IRSs) usually suffer from a low ability to recognize a same idea that is expressed in different forms. A way of improving these systems is to take into account morphological variants. We propose here a simple yet effective method to recognize these variants that are further used so as to enrich queries. In comparison with already published methods, our system does not need any external resources or a priori knowledge and thus supports many languages. This new approach is evaluated against several collections, 6 different languages and is compared to existing tools such as a stemmer and a lemmatizer. Reported results show a significant and systematic improvement of the whole IRS efficiency both in terms of precision and recall for every language.