statistical ngram language models

... using 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 ... relative to each other. 4.1 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 ... of syntax. Our approach uses n- gram language models as a low-cost automatic ap- proximation of both syntactic and semantic analy- sis. Statistical language models (LMs) are used suc- cessfully...

Ngày tải lên: 20/02/2014, 15:20

... 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 ... comparison of in- grammar recognition performance. 3 Language modelling To generate the different trigram language models we used the SRI language modelling toolkit (Stol- cke, 2002) with Good-Turing ... move specific statistical language models (DM-SLMs) by using GF to generate all utterances that are specific to certain dialogue moves from our in- terpretation grammar. In this way we can pro- duce models...

Ngày tải lên: 22/02/2014, 02:20

... of statistical machine translation: Parameter estimation. Computa- tional Linguistics, 19(2):263–311. Eugene Charniak, Kevin Knight, and Kenji Yamada. 2003. Syntax-based language models for statistical machine ... as language models for statistical machine translation. In Proceed- ings of AMTA. Sylvain Raybaud, Caroline Lavecchia, David Langlois, and Kamel Sma ¨ ıli. 2009. New confidence measures for statistical ... Computational Linguistics Enhancing Language Models in Statistical Machine Translation with Backward N-grams and Mutual Information Triggers Deyi Xiong, Min Zhang, Haizhou Li Human Language Technology Institute...

Ngày tải lên: 07/03/2014, 22:20

Ngày tải lên: 08/03/2014, 04:22

Ngày tải lên: 08/03/2014, 21:20

Ngày tải lên: 31/03/2014, 01:20

... research in statistical machine trans- lation has effectively used n-gram word sequence models as language models. Modern phrase-based translation using large scale n-gram language models generally ... to 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 ... translation model. Instead, we incor- porate syntax into the language model. Traditional approaches to language models in speech recognition and statistical machine transla- tion focus on the use of...

Ngày tải lên: 20/02/2014, 04:20

... language models trained from text or speech corpora of vari- ous genres and sizes. The largest available language models are based on written text: we investigate the effect of written text language models ... dif- ferences among the different language models when extended features are present are relatively small. We assume that much of the information expressed in the language models overlaps with the lexical ... information from the external language models by defining a reranker feature for each external language model. The value of this feature is the log probability assigned by the language model to the candidate...

Ngày tải lên: 20/02/2014, 04:20

... 2006. MAP adaptation of stochastic grammars. Computer Speech & Language, 20(1):41 – 68. Jerome R. Bellegarda. 2004. Statistical language model adaptation: review and perspectives. Speech Commu- nication, ... 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 ... N-gram Language Models Based on Ordinary Counts. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 349–352. Ronald Rosenfeld. 1996. A Maximum Entropy Ap- proach to Adaptive Statistical...

Ngày tải lên: 20/02/2014, 04:20

... 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. ... perplexity of any known 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. ... best 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....

Ngày tải lên: 20/02/2014, 09:20

... framework, real-time is re- quired. 4 Phrase-based models The usual statistical translation models can be classified as single-word based alignment models. Models of this kind assume that an input word ... framework (Och et al., 2003). Phrase-based models have proved to be very ad- equate statistical models for MT (Tom ´ as et al., 2005). In this work, the use of these models has been extended to interactive ... Therefore, the same techniques (translation models, decoder al- gorithm, etc.) which have been developed for SMT can be used in CAT. Note that the statistical models are defined at word level. However,...

Ngày tải lên: 20/02/2014, 12:20

... max- imum entropy models for statistical machine translation. In Proc. of ACL, pages 295–302. F. J. Och and H. Ney. 2003. A systematic comparison of vari- ous statistical alignment models. Computational ... Constrained Word Alignment Models The framework that we propose to incorporate sta- tistical constraints into word alignment models is generic. It can be applied to complicated models such IBM Model-4 ... translation performance. 1 Introduction Statistical word alignment models learn word as- sociations between parallel sentences from statis- tics. Most models are trained from corpora in an unsupervised...

Ngày tải lên: 20/02/2014, 12:20

... "Class-based n-gram models of natural language. " Computa- tional Linguistics, 18(4), pages 467-479. D. M. Magerman. 1994. Natural Language Pars- ing as Statistical Pattern Recognition. ... One of the important points of this work is that statistical models of natural language should not be restricted to simple, context-insensitive models. In a problem like parsing, where long-distance ... SPATTER's models SPATTER consists of three main decision-tree models: a part-of-speech tagging model, a node- extension model, and a node-labeling model. Each of these decision-tree models are...

Ngày tải lên: 20/02/2014, 22:20

... 2007. Large language models in machine translation. In Proceedings of the 2007 Joint Conference on Empirical Meth- ods in Natural Language Processing and Com- putational Natural Language Learning ... Kneser- Ney smoothed n-gram models. IEEE Transac- tions on Audio, Speech and Language Processing, 15(5):1617–1624. A. Stolcke. 1998. Entropy-based pruning of backoff language models. In Proc. DARPA ... were selected for each language. The adaptation was thought to take place off-line on a server. 3.2.1 Data sets For each language, the adaptation takes place on two baseline models, which are the...

Ngày tải lên: 22/02/2014, 02:20

... grammars for modeling agglutination in this language, but first we will present the for- mer class of languages and its acceptor automata. 3.1 Linear context free languages and two-taped nondeterministic ... 2010. c 2010 Association for Computational Linguistics The use of formal language models in the typology of the morphology of Amerindian languages Andr ´ es Osvaldo Porta Universidad de Buenos Aires hugporta@yahoo.com.ar Abstract The ... natural representa- tion in terms of linear context-free languages. 2 Quichua Santiague ˜ no The quichua santiague˜no is a language of the Quechua language family. It is spoken in the San- tiago del...

Ngày tải lên: 07/03/2014, 22:20

... novel language model caching technique that improves the query speed of our language models (and SRILM) by up to 300%. 1 Introduction For modern statistical machine translation systems, language models ... with two different language models. Our first language model, WMT2010, was a 5- gram Kneser-Ney language model which stores probability/back-off pairs as values. We trained this language model on ... and Smaller N -Gram Language Models Adam Pauls Dan Klein Computer Science Division University of California, Berkeley {adpauls,klein}@cs.berkeley.edu Abstract N-gram language models are a major...

Ngày tải lên: 07/03/2014, 22:20

... 2007. Compressing trigram language models with golomb coding. In Proceedings of EMNLP-CoNLL 2007, Prague, Czech Republic, June. P. Clarkson and R. Rosenfeld. 1997 . Statistical language modeling using ... 2007a. Randomised language modelling for statistical machine translation. In 45th Annual Meeting of the ACL 2007, Prague. D. Talbot and M. Osborne. 2007b. Smoothed Bloom filter language models: Tera-scale ... alignment template approach to statistical machine translation. Computational Linguistics, 30(4):417–449. Andreas Stolcke. 1998. Entropy-based pruning of back- off language models. In Proc. DARPA Broadcast...

Ngày tải lên: 08/03/2014, 01:20

... linear models via a dynamic program. In the CMM, the local linear models are trained independently, while in the CRF model, the local models are trained jointly. We call these two linear models ... non-zero weight. 3.2 Language model adaptation Our experiments with LM adaptation are based on the work described in Gao et al. (2006). The va- riously trained language models were evaluated ... linear models local models because they dynamically combine the output of models that use only local features. While it is straightforward to apply the five es- timators to global models in the...

Ngày tải lên: 08/03/2014, 02:21

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Ngày tải lên: 08/03/2014, 21:20