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Exploring Issues in Lexical Acquisition Using Bayesian Modeling ...
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Exploring Issues in Lexical Acquisition Using Bayesian Modeling
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A Computationally efficient algorithm for learning topical collocation models
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Unsupervised word segmentation in context
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Abstract:
This paper extends existing word segmentation models to take non-linguistic context into account. It improves the token F-score of a top performing segmentation models by 2.5% on a 27k utterances dataset. We posit that word segmentation is easier in-context because the learner is not trying to access irrelevant lexical items. We use topics from a Latent Dirichlet Allocation model as a proxy for "activities" contexts, to label the Providence corpus. We present Adaptor Grammar models that use these context labels, and we study their performance with and without context annotations at test time. ; 9 page(s)
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URL: http://hdl.handle.net/1959.14/336964
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Exploring the role of stress in Bayesian word segmentation using adaptor grammars
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Whyisenglishsoeasytosegment? ; Why is English so easy to segment?
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A Joint model of word segmentation and phonological variation for English word-final t-deletion
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Studying the effect of input size for Bayesian word segmentation on the providence corpus
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Using rejuvenation to improve particle filtering for Bayesian word segmentation
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