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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 12 of 12 1 Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources ... Milintsevich, Kirill; Sirts, Kairit. - : arXiv, 2021 BASE Show details 2 Evaluating Multilingual BERT for Estonian ... Kittask, Claudia; Milintsevich, Kirill; Sirts, Kairit. - : arXiv, 2020 BASE Show details 3 EstBERT: A Pretrained Language-Specific BERT for Estonian ... Tanvir, Hasan; Kittask, Claudia; Eiche, Sandra. - : arXiv, 2020 BASE Show details 4 STransE: a novel embedding model of entities and relationships in knowledge bases ... Nguyen, Dat Quoc; Sirts, Kairit; Qu, Lizhen. - : arXiv, 2016 BASE Show details 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 BASE Show details 6 Query-based single document summarization using an Ensemble Noisy Auto-Encoder Yousefi Azar, Mahmood; Sirts, Kairit; Molla Aliod, Diego; Hamey, Len. - : Melbourne, Australia : Association for Computational Linguistics, 2015 Abstract: In this paper we use a deep auto-encoder for extractive query-based summarization. We experiment with different input representations in order to overcome the problems stemming from sparse inputs characteristic to linguistic data. In particular, we propose constructing a local vocabulary for each document and adding a small random noise to the input. Also, we propose using inputs with added noise in an Ensemble Noisy Auto-Encoder (ENAE) that combines the top ranked sentences from multiple runs on the same input with different added noise. We test our model on a publicly available email dataset that is specifi- cally designed for text summarization. We show that although an auto-encoder can be a quite effective summarizer, adding noise to the input and running a noisy ensemble can make improvements ; 9 page(s) URL: http://hdl.handle.net/1959.14/1057814 BASE Hide details 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 BASE Show details 8 Minimally-supervised morphological segmentation using adaptor grammars Sirts, Kairit; Goldwater, Sharon. - : Association for Computational Linguistics, 2013 BASE Show details 9 Noisy-channel spelling correction models for Estonian learner language corpus lemmatisation Sirts, Kairit. - : Amsterdam : IOS Press, 2012 BASE Show details 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 BASE Show details 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 BASE Show details 12 Eesti silbisüsteemi struktuurist ; A preliminary structural view of the Estonian syllable system Võhandu, Leo; Sirts, Kairit; Aab, Eik. - : Eesti Rakenduslingvistika Uhing, 2008 BASE Show details

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