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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 – 10 of 10 1 Maximum Entropy Language Modeling with Non-Local and Syntactic Dependencies Jun Wu; Advisor Dr. Sanjeev Khudanpur In: http://www.cs.jhu.edu/~junwu/gbo.ps (2002) BASE Show details 2 Smoothing Issues in the Structured Language Model Woosung Kim; Sanjeev Khudanpur; Jun Wu In: http://cs.jhu.edu/~junwu/eurospeech01.ps (2001) BASE Show details 3 Smoothing Issues in the Structured Language Mode Woosung Kim; Sanjeev Khudanpur; Jun Wu In: http://www.clsp.jhu.edu/~woosung/pdf/euro01.pdf (2001) BASE Show details 4 Syntactic Heads In Statistical Language Modeling Jun Wu; Sanjeev Khudanpur In: http://www.cs.jhu.edu/~junwu/icassp2000.ps (2000) BASE Show details 5 Combining Nonlocal, Syntactic And N-Gram Dependencies In Language Modeling Jun Wu; Sanjeev Khudanpur In: http://www.cs.jhu.edu/~junwu/eurospeech.ps (1999) BASE Show details 6 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) BASE Show details 7 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) BASE Show details 8 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) BASE Show details 9 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) Abstract: Maximum entropy (ME) techniques have been successfully used to combine different sources of linguistically meaningful constraints in language models. However, most of the current ME models can only be used for small corpora, since the computational load in training ME models for large corpora is unbearable. This problem is especially severe when non-local dependencies are considered. In this paper, we show how to train and use topic-dependent ME models efficiently for a very large corpus, Broadcast News (BN). The training time is greatly reduced by hierarchical training and divide-and-conquer approaches. The computation in using the model is also simplified by pre-normalizing the denominators of the ME model. We report new speech recognition results showing improvement with the topic model relative to the standard N-gram model for the Broadcast News task. URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.9889 http://www.cs.jhu.edu/~junwu/icassp02.ps BASE Hide details 10 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 BASE Show details

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