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Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon.

, , , , , and . Cogn. Sci., 42 (3): 939-973 (2018)

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Language Identification of Short Text Segments with N-gram Models., , and . LREC, European Language Resources Association, (2010)First-Pass Techniques for Very Large Vocabulary Speech Recognition ff Morphologically Rich Languages., , and . SLT, page 227-234. IEEE, (2018)Applying Morphological Decompositions to Statistical Machine Translation., , , and . WMT@ACL, page 195-200. Association for Computational Linguistics, (2010)Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian., , and . Comput. Speech Lang., (2021)Morpho Challenge 2005-2010: Evaluations and Results., , , and . SIGMORPHON, page 87-95. Association for Computational Linguistics, (2010)Adaptive Translation: Finding Interlingual Mappings Using Self-Organizing Maps., , and . ICANN (1), volume 5163 of Lecture Notes in Computer Science, page 603-612. Springer, (2008)Class n-Gram Models for Very Large Vocabulary Speech Recognition of Finnish and Estonian., , and . SLSP, volume 9918 of Lecture Notes in Computer Science, page 133-144. (2016)Evaluating vector space models with canonical correlation analysis., , , , and . Nat. Lang. Eng., 18 (3): 399-436 (2012)LeBLEU: N-gram-based Translation Evaluation Score for Morphologically Complex Languages., and . WMT@EMNLP, page 411-416. The Association for Computer Linguistics, (2015)The University of Helsinki Submissions to the WMT19 News Translation Task., , , , , , , and . WMT (2), page 412-423. Association for Computational Linguistics, (2019)