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1
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources ...
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2
Evaluating Multilingual BERT for Estonian ...
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3
EstBERT: A Pretrained Language-Specific BERT for Estonian ...
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4
STransE: a novel embedding model of entities and relationships in knowledge bases ...
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5
STransE : a novel embedding model of entities and relationships in knowledge bases
Nguyen, Dat Quoc; Sirts, Kairit; Qu, Lizhen. - : Red Hook, New York : Association for Computational Linguistics, 2016
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6
Query-based single document summarization using an Ensemble Noisy Auto-Encoder
Yousefi Azar, Mahmood; Sirts, Kairit; Molla Aliod, Diego. - : Melbourne, Australia : Association for Computational Linguistics, 2015
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7
POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process
Sirts, Kairit; Eisenstein, Jacob; Elsner, Micha. - : Stroudsburg, PA : Association for Computational Linguistics, 2014
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8
Minimally-supervised morphological segmentation using adaptor grammars
Sirts, Kairit; Goldwater, Sharon. - : Association for Computational Linguistics, 2013
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9
Noisy-channel spelling correction models for Estonian learner language corpus lemmatisation
Sirts, Kairit. - : Amsterdam : IOS Press, 2012
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10
A Hierarchical dirichlet process model for joint part-of-speech and morphology induction
Alumäe, Tanel; Sirts, Kairit. - : Stroudsburg, PA : Association for Computational Linguistics, 2012
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11
Korpuste tükeldamine : rakendusi silpide ning allkeeltega ; Cutting the text corpora : applications with syllables and sub-languages
Sirts, Kairit; Võhandu, Leo. - : Eesti Rakenduslingvistika Uhing = Estonian Association for Applied Linguistics, 2009
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12
Eesti silbisüsteemi struktuurist ; A preliminary structural view of the Estonian syllable system
Võhandu, Leo; Sirts, Kairit; Aab, Eik. - : Eesti Rakenduslingvistika Uhing, 2008
Abstract: Using the Corpus of Estonian Literary Language and specifically the selection of fiction texts from years 1988-1998 we have studied the frequency table of Estonian syllables (1000 most frequent syllables). As an experimental study we have used Hamilton Path (HP) and Social Network Clusters to study the inner structure of syllable system. For HP we have created and represented a program in J-6.01 which connects all syllables into a sequential path with the maximal connections sum and without a syllable repetition. As another method we have created SNC networks for 100 and 1000 most frequent syllables. In this short article we have presented graphically only the 100 syllable network without explaining exactly either the mathematical method or networks interpretation. ; 7 page(s)
Keyword: computational linguistics; Estonian; graph representation; Hamilton path; syllabification; syllable association; syllable social network
URL: http://hdl.handle.net/1959.14/1089057
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