Viewing morphology as an inference process
SIGIR '93 Proceedings of the 16th 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
Corpus-based stemming using cooccurrence of word variants
ACM Transactions on Information Systems (TOIS)
An algorithm for suffix stripping
Readings in information retrieval
Probabilistic models of information retrieval based on measuring the divergence from randomness
ACM Transactions on Information Systems (TOIS)
CLEF Experiments at Maryland: Statistical Stemming and Backoff Translation
CLEF '00 Revised Papers from the Workshop of Cross-Language Evaluation Forum on Cross-Language Information Retrieval and Evaluation
Strength and similarity of affix removal stemming algorithms
ACM SIGIR Forum
Character N-Gram Tokenization for European Language Text Retrieval
Information Retrieval
Unsupervised learning of the morphology of a natural language
Computational Linguistics
A probabilistic model for stemmer generation
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
YASS: Yet another suffix stripper
ACM Transactions on Information Systems (TOIS)
Searching strategies for the Hungarian language
Information Processing and Management: an International Journal
Bulgarian, Hungarian and Czech Stemming Using YASS
Advances in Multilingual and Multimodal Information Retrieval
Indexing and stemming approaches for the Czech language
Information Processing and Management: an International Journal
Comparative Study of Indexing and Search Strategies for the Hindi, Marathi, and Bengali Languages
ACM Transactions on Asian Language Information Processing (TALIP)
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Effective and Robust Query-Based Stemming
ACM Transactions on Information Systems (TOIS)
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A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retrieval effectiveness, generality, and computational efficiency. We test our approach on seven languages (using collections from the TREC, CLEF, and FIRE evaluation platforms) of varying morphological complexity. Significant performance improvement over plain word-based retrieval, three other language-independent morphological normalizers, as well as rule-based stemmers is demonstrated.