succinct ngram language model

... -gram language models are com- pressed into 10 GB, which is comparable to a lossy representation (Talbot and Brants, 2008). 2 N -gram Language Model We assume a back-off N-gram language model ... GB Succinct 12.62 GB 10.57 GB Language Trie 42.65 GB 20.01 GB model Integer 33.65 GB 18.98 GB Succinct 31.67 GB 18.37 GB Quantized Trie 24.73 GB 11.47 GB language Integer 15.73 GB 10.44 GB model ... 23.59GB to 10.57GB by our succinct representation with block compression. N-gram language models of 42.65GB were compressed to 18.37GB. Finally, the 8-bit quantized N -gram language models are represented...

Ngày tải lên: 20/02/2014, 09:20

... that the n-gram language model used by the MT system was much smaller than the 5-GRAM model, as they were only trained on the English sides of their parallel data. fect language model might not ... United States were asked to compare translations using our TREELET language model as the language model feature to those using the 5-GRAM model. 12 We had 1000 such translation pairs rated by 4 separate ... TREELET model, we also show results for the following baselines: 5-GRAM A 5-gram interpolated Kneser-Ney model. PCFG-LA The Berkeley Parser in language model mode. HEADLEX A head-lexicalized model...

Ngày tải lên: 19/02/2014, 19:20

... 5-gram/2-SLM+2-gram/4-SLM+5- gram/PLSA language model improves both signif- icantly. Bear in mind that Charniak et al. (2003) in- tegrated Charniak’s language model with the syntax- based translation model Yamada and ... Large language models in ma- chine translation. The 2007 Conference on Empirical Methods in Natural Language Processing (EMNLP), 858-867. E. Charniak. 2001. Immediate-head parsing for language models. ... Dis- tributed language modeling for N-best list re-ranking. The 2006 Conference on Empirical Methods in Natu- ral Language Processing (EMNLP), 216-223. Y. Zhang, 2008. Structured language models for...

Ngày tải lên: 20/02/2014, 04:20

... incorporate large- scale n-gram language models in conjunction with incremental syntactic language models. The added decoding time cost of our syntactic language model is very high. By increasing ... sequence models as language models. Modern phrase-based translation using large scale n-gram language models generally performs well in terms of lexical choice, but still often produces ungrammatical ... parsing for language modeling, but do not use this language model in a translation system. Our work, in contrast to the above approaches, explores the use of incremental syntactic language models...

Ngày tải lên: 20/02/2014, 04:20

... f-score outperform state-of-the-art models re- 709 mary corpus for our model. The language model part of the noisy channel model already uses a bi- gram language model based on Switchboard, but in the ... productive. Indeed the best per- forming model is the model which has all extended features and all language model features. The dif- ferences among the different language models when extended features ... 19 other folds to construct a language model and then score the utterance in this fold with that language model. The largest widely-available corpus for language modelling is the Web 1T 5-gram...

Ngày tải lên: 20/02/2014, 04:20

... likelihood of the test corpus under the train corpus language model (using basic Kneser-Ney) and the likelihood of the test corpus under a jackknife language model from the test itself, which holds out ... of English Bigrams. Computer Speech & Language, 5(1):19–54. Joshua Goodman. 2001. A Bit of Progress in Language Modeling. Computer Speech & Language, 15(4):403– 434. Bo-June (Paul) Hsu ... M-Gram Language Modeling. In Pro- ceedings of International Conference on Acoustics, Speech, and Signal Processing. Robert C. Moore and William Lewis. 2010. Intelligent selection of language model...

Ngày tải lên: 20/02/2014, 04:20

... accurate language model. For the current study, all language models were estimated from a one million sentence (210M char- acter) sample of the NY Times portion of the English Gigaword corpus. Models ... before, the user can take a break and then the system continues with the next epoch. 3 Language Modeling Language modeling is important for many text pro- cessing applications, e.g., speech recognition ... simple interfaces have yet to take full advan- tage of language models to ease or speed typing. In this demonstration, we will present a language- model enabled interface that is appropriate for the most...

Ngày tải lên: 20/02/2014, 05:20

... probabilities (PPs), which combines acoustic model and language model scores after decoding. Based on the character PPs, we adapt the current lexicon. The language model is then re-trained ac- cording ... and Language Model Training If we regard the word segmentation process as a hidden variable, then we can apply EM algorithm (Dempster et al., 1977) to train the underlying n- gram language model. ... words and also the building units in the language model (LM). Lexical words offer local constraints to combine phonemes into short chunks while the language model combines phonemes into longer chunks...

Ngày tải lên: 20/02/2014, 07:20

... Goodman. 2001. A bit of progress in language modeling. Computer Speech and Language. R. Kneser and H. Ney. 1995. Improved backing-off for m-gram language modeling. In International Confer- ence ... Bauman Peto. 1995. A hierarchical Dirichlet language model. Natural Lan- guage Engineering, 1(3):1–19. Y.W. Teh. 2006. A hierarchical Bayesian language model based on Pitman-Yor processes. In Proceed- ings ... sure the probabilities are normalized. The interpolated models always incorporate the lower or- der distribution Pr(c|b) whereas the back-off models consider it only when the n-gram abc has not been observed...

Ngày tải lên: 20/02/2014, 09:20

... method for estimating N-gram language models. Kneser-Ney smoothing, however, requires nonstandard N-gram counts for the lower- order models used to smooth the highest- order model. For some applications, ... Kneser-Ney and those methods. 1 Introduction Statistical language models are potentially useful for any language technology task that produces natural -language text as a final (or intermediate) output. ... approach when language models based on ordinary counts are desired. References Chen, Stanley F., and Joshua Goodman. 1998. An empirical study of smoothing techniques for language modeling. Technical...

Ngày tải lên: 20/02/2014, 09:20

... syntactic language models into a speech recognizer. These methods have almost ex- clusively worked within the noisy channel paradigm, where the syntactic language model has the task of modeling ... Jelinek. 2000. Structured language modeling. Computer Speech and Language, 14(4):283–332. Ciprian Chelba. 2000. Exploiting Syntactic Structure for Nat- ural Language Modeling. Ph.D. thesis, The ... pass on these first pass lattices, allowing for better silence modeling, and replaces the trigram language model score with a 6-gram model. 1000-best lists were then extracted from these lattices....

Ngày tải lên: 20/02/2014, 15:20

... statistical language modeling, and language identification. A typical LID system is illustrated in Figure 1 (Zissman, 1996), where language dependent voice tokenizers (VT) and lan- guage models ... Tokenization at different resolutions 2.2 n-gram Language Model With the sequence of tokens, we are able to es- timate an n-gram language model (LM) from the statistics. It is generally agreed ... the 1996 NIST Language Recognition Evaluation database. 1 Introduction Spoken language and written language are similar in many ways. Therefore, much of the research in spoken language identification,...

Ngày tải lên: 20/02/2014, 15:20

... Good- man, 1999). We used the SRI Language Modeling Toolkit (Stolcke, 2002) for language model training. Our first set of classifiers consists of one n-gram language model per class c in the set of ... Statistical Language Models Statistical LMs predict the probability that a partic- ular word sequence will occur. The most commonly used statistical language model is the n-gram model, which ... statistical language models. In this paper, we also use support vector machines to combine features from tradi- tional reading level measures, statistical language models, and other language pro- cessing...

Ngày tải lên: 20/02/2014, 15:20

... efficient. 5 Experiments 5.1 Training Data for the Language Model We used the EDR Japanese Corpus Version 1.0 (EDR, 1991) to train the language model. It is a corpus of approximately 5.1 million ... dictionary. 3.2 Word Model for Unknown Words We defined a statistical word model to assign a rea- sonable word probability to an arbitrary substring in the input sentence. The word model is formally ... correction candidate is selected by the word segmentation algo- rithm using the OCR model and the language model. For simplicity, we will present the method as if it were for an isolated word...

Ngày tải lên: 20/02/2014, 18:20

... performance. 3 Language modelling To generate the different trigram language models we used the SRI language modelling toolkit (Stol- cke, 2002) with Good-Turing discounting. The first model was generated ... decades of statistical language modeling: Where do we go from here? In Proceed- ings of IEEE:88(8). Rosenfeld R. 2000. Incorporating Linguistic Structure into Statistical Language Models. In Philosophical Transactions ... Statistical Language Modeling Using the CMU-Cambridge Toolkit. In Proceedings of Eurospeech. Fosler-Lussier E. and Kuo H K. J. 2001. Using Se- mantic Class Information for Rapid Development of Language Models...

Ngày tải lên: 22/02/2014, 02:20

Ngày tải lên: 22/02/2014, 02:20

Ngày tải lên: 22/02/2014, 03:20

Ngày tải lên: 07/03/2014, 22:20

Ngày tải lên: 07/03/2014, 22:20

Ngày tải lên: 07/03/2014, 22:20