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What transfers in morphological inflection? Experiments with analogical models ...
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Formalizing Inflectional Paradigm Shape with Information Theory
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In: Proceedings of the Society for Computation in Linguistics (2021)
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The Paradigm Discovery Problem
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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Interpreting Sequence-to-Sequence Models for Russian Inflectional Morphology
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Stop the Morphological Cycle, I Want to Get Off: Modeling the Development of Fusion
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Normalization may be ineffective for phonetic category learning ...
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Normalization may be ineffective for phonetic category learning
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Giving Good Directions: Order of Mention Reflects Visual Salience
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Giving Good Directions: Order of Mention Reflects Visual Salience
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POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process
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Where's Wally: the influence of visual salience on referring expression generation
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Structured generative models for unsupervised named-entity clustering
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Abstract:
We describe a generative model for clustering named entities which also models named entity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference information from an unsupervised pronoun resolver. The model scores 86% on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsupervised model, and the best score for a generative model. ; 9 page(s)
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Keyword:
080100 Artificial Intelligence and Image Processing
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URL: http://hdl.handle.net/1959.14/323580
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