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Brief Stimulus Exposure Fully Remediates Temporal Processing Deficits Induced by Early Hearing Loss
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Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM,” in INTERSPEECH
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In: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/IS16_MultiJoint.pdf (2016)
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EVALUATION OF THE SIGNAL-TO-NOISE RATIO REQUIRED TO ACHIEVE THE SAME PERFORMANCE IN ENGLISH AND MANDARIN CHINESE
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Semi-supervised learning of semantic classes for query . . .
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In: http://research.microsoft.com/pubs/101154/fp0894-wang-webpost.pdf (2009)
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Acero: A discriminative training framework using N-best speech recognition transcriptions and scores for spoken utterance classification
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In: http://www1.icsi.berkeley.edu/~sibel/ICASSP2007SUC.pdf (2007)
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SGStudio: Rapid Semantic Grammar Development for Spoken Language Understanding
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In: http://research.microsoft.com/pubs/60452/2005-wang-acero-eurospeech.pdf (2005)
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Grammar Inference and Statistical Machine Translation
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In: http://www.is.cs.cmu.edu/papers/speech/phd-thesis/thesis-yyw.ps.gz (1998)
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Decoding Algorithm in Statistical Machine Translation
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In: http://www.ri.cmu.edu/pub_files/pub1/wang_ye_yi_1997_1/wang_ye_yi_1997_1.ps.gz (1997)
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Word Clustering With Parallel Spoken Language Corpora
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In: http://research.microsoft.com/users/yeyiwang/publications/icslp96.ps (1996)
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Word Clustering With Parallel Spoken Language Corpora
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In: http://www.ubka.uni-karlsruhe.de/vvv/1996/informatik/66/66.ps.gz (1996)
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Word clustering with parallel spoken language corpora
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In: http://www.ri.cmu.edu/pub_files/pub1/wang_ye_yi_1996_1/wang_ye_yi_1996_1.pdf (1996)
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Word clustering with parallel spoken language corpora
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In: http://research.microsoft.com/pubs/75238/1996-yeyiwang-icslp.pdf (1996)
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Word Clustering With Parallel Spoken Language Corpora
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In: http://www.asel.udel.edu/icslp/cdrom/vol4/687/a687.pdf (1996)
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Improved Language Modeling By Unsupervised Acquisition Of Structure
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In: ftp://ftp.cs.cmu.edu/afs/cs/project/cmt-38/ries/ftp/ries_buo_wang_icassp95.ps.gz (1995)
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Abstract:
The word accuracy of the decoder is improved from 59:1% to 61:1% by a new method for the unsupervised acquisition of a structural text model. Well recognized sentences even show an improvement from 84:8% to 91:1%. This method is based on new algorithms for the classification of words and phrases by their context and on new sequence finding procedures. These procedures are designed to work fast and accurately on small and large corpora. They are iterated to build a structural model of a corpus. Further applications in language processing, which exploit the structure finding capabilities of this model, such as preprocessing for neural networks and (hidden) markov models, are proposed. 1. CLASSIFYING ENTITIES FROM CONTEXT VIA ITERATED REESTIMATION The most widespread criterion for the classification of words and phrases in linguistics is the replacement test, which states, that two linguistic entities are the same, if they can be mutually substituted in sentences where they appear. [Fin.
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.5838
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Improved Language Modeling By Unsupervised Acquisition Of Structure
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In: http://werner.ira.uka.de/papers/speech/1995/ICASSP_95_klaus_ries.ps.gz (1995)
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Connectionist Transfer in Machine Translation
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In: http://research.microsoft.com/users/yeyiwang/publications/ranlp95.ps (1995)
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Dual-coding theory and connectionist lexical selection
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In: http://arxiv.org/pdf/cmp-lg/9405035v1.pdf (1994)
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