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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 – 9 of 9 1 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : arXiv, 2020 BASE Show details 2 A corpus for large-scale phonetic typology Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - 2020 BASE Show details 3 Contextualization of Morphological Inflection ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2019 BASE Show details 4 Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model ... Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 BASE Show details 5 On the Complexity and Typology of Inflectional Morphological Systems ... Cotterell, Ryan; Kirov, Christo; Hulden, Mans. - : arXiv, 2018 BASE Show details 6 Are All Languages Equally Hard to Language-Model? ... Cotterell, Ryan; Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 BASE Show details 7 A Deep Generative Model of Vowel Formant Typology ... Cotterell, Ryan; Eisner, Jason. - : arXiv, 2018 BASE Show details 8 Unsupervised Disambiguation of Syncretism in Inflected Lexicons ... Cotterell, Ryan; Kirov, Christo; Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 Abstract: Lexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token's context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages. ... : Published at NAACL 2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1806.03740 https://dx.doi.org/10.48550/arxiv.1806.03740 BASE Hide details 9 Probabilistic Typology: Deep Generative Models of Vowel Inventories ... Cotterell, Ryan; Eisner, Jason. - : arXiv, 2017 BASE Show details

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