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Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources ...
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EstBERT: A Pretrained Language-Specific BERT for Estonian ...
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STransE: a novel embedding model of entities and relationships in knowledge bases ...
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STransE : a novel embedding model of entities and relationships in knowledge bases
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Query-based single document summarization using an Ensemble Noisy Auto-Encoder
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POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process
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Minimally-supervised morphological segmentation using adaptor grammars
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Noisy-channel spelling correction models for Estonian learner language corpus lemmatisation
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A Hierarchical dirichlet process model for joint part-of-speech and morphology induction
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Abstract:
In this paper we present a fully unsupervised nonparametric Bayesian model that jointly induces POS tags and morphological segmentations. The model is essentially an infinite HMM that infers the number of states from data. Incorporating segmentation into the same model provides the morphological features to the system and eliminates the need to find them during preprocessing step. We show that learning both tasks jointly actually leads to better results than learning either task with gold standard data from the other task provided. The evaluation on multilingual data shows that the model produces state-of-the-art results on POS induction. ; 10 page(s)
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URL: http://hdl.handle.net/1959.14/1148085
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Korpuste tükeldamine : rakendusi silpide ning allkeeltega ; Cutting the text corpora : applications with syllables and sub-languages
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Eesti silbisüsteemi struktuurist ; A preliminary structural view of the Estonian syllable system
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