Factored language models and generalized parallel backoff
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Unsupervised discovery of morphemes
MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Morpho Challenge Evaluation Using a Linguistic Gold Standard
Advances in Multilingual and Multimodal Information Retrieval
Morphological analysis for statistical machine translation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Overview of Morpho challenge 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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In Morpho Challenge competitions, the objective has been to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are basic vocabulary units suitable for different tasks, such as text understanding, machine translation, information retrieval (IR), and statistical language modeling. In this paper, we propose to evaluate the morpheme analyses by performing IR experiments, where the words in the documents and queries are replaced by their proposed morpheme representations and the search is based on morphemes instead of words. In this paper, the evaluations are run for three languages: Finnish, German, and English using the queries, texts, and relevance judgments available in CLEF forum. The results show that the morpheme analysis has a significant effect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods.