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language modeling (1)
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Hits 1 – 9 of 9
1
Smoothing Issues in the Structured Language Model
Woosung Kim
;
Sanjeev Khudanpur
;
Jun Wu
In: http://cs.jhu.edu/~junwu/eurospeech01.ps (2001)
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2
Smoothing Issues in the Structured Language Mode
Woosung Kim
;
Sanjeev Khudanpur
;
Jun Wu
In: http://www.clsp.jhu.edu/~woosung/pdf/euro01.pdf (2001)
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3
Syntactic Heads In Statistical Language Modeling
Jun Wu
;
Sanjeev Khudanpur
In: http://www.cs.jhu.edu/~junwu/icassp2000.ps (2000)
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4
Combining Nonlocal, Syntactic And N-Gram Dependencies In Language Modeling
Jun Wu
;
Sanjeev Khudanpur
In: http://www.cs.jhu.edu/~junwu/eurospeech.ps (1999)
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5
A Maximum Entropy Language Model Integrating N-Grams And Topic Dependencies For Conversational Speech Recognition
Sanjeev Khudanpur
;
Jun Wu
In: http://www.cs.jhu.edu/~junwu/topic-lm.ps (1999)
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6
A maximum entropy language model integrating ngrams and topic dependencies for conversational speech reconition
Sanjeev Khudanpur
;
Jun Wu
In: http://www.mirlab.org/conference_papers/International_Conference/ICASSP 1999/PDF/AUTHOR/IC992192.PDF (1999)
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7
A Maximum Entropy Language Model with Topic Sensitive Features
Jun Wu
;
Advisors Dr
;
Sanjeev Khudanpur
...
In: http://www.cs.jhu.edu/~junwu/memodel.ps (1998)
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8
Building A Topic-Dependent Maximum Entropy Model For Very Large Corpora
Jun Wu
;
Sanjeev Khudanpur
In: http://www.cs.jhu.edu/~junwu/icassp02.ps (1217)
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9
Maximum Entropy Techniques for Exploiting Syntactic, Semantic and Collocational Dependencies in Language Modeling
Sanjeev Khudanpur
;
Jun Wu
In: http://www.cs.jhu.edu/~junwu/cslpaper.ps
Abstract:
A new statistical language model is presented which combines collocational dependencies with two important sources of long-range statistical dependence: the syntactic structure and the topic of a sentence. These dependencies or constraints are integrated using the maximum entropy technique. Substantial improvements are demonstrated over a trigram model in both perplexity and speech recognition accuracy on the Switchboard task. A detailed analysis of the performance of this language model is provided in order to characterize the manner in which it performs better than a standard N-gram model. It is shown that topic dependencies are most useful in predicting words which are semantically related by the subject matter of the conversation. Syntactic dependencies on the other hand are found to be most helpful in positions where the best predictors of the following word are not within N-gram range due to an intervening phrase or clause. It is also shown that these two methods ind.
URL:
http://www.cs.jhu.edu/~junwu/cslpaper.ps
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.8552
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