smoothing ngram language models

... USA {bobmoore,chrisq}@microsoft.com Abstract Kneser-Ney (1995) smoothing and its vari- ants are generally recognized as having the best perplexity of any known method for estimating N-gram language models. Kneser-Ney smoothing, however, ... 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. ... lower- order models used to smooth the highest- order model. For some applications, this makes Kneser-Ney smoothing inappropri- ate or inconvenient. In this paper, we in- troduce a new smoothing...

Ngày tải lên: 20/02/2014, 09:20

... 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 ... 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 ... Jelinek. 2000. Structured language modeling. Computer Speech and Language, 14(4):283–332. Stanley F. Chen and Joshua Goodman. 1998. An empir- ical study of smoothing techniques for language mod- eling....

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

... 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 ... Short Papers, pages 220–224, July. Robert C. Moore and Chris Quirk. 2009. Improved Smoothing for N-gram Language Models Based on Ordinary Counts. In Proceedings of the ACL-IJCNLP 2009 Conference ... 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,...

Ngày tải lên: 20/02/2014, 04:20

... 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 ... use scores from language models as features in another classifier (e.g. an SVM). For ex- ample, perplexity (P P) is an information-theoretic measure often used to assess language models: P P = 2 H(t|c) , ... 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

... 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 ... 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 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 that...

Ngày tải lên: 22/02/2014, 02:20

... 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 ... empirical study of smoothing techniques for language model- ing. Computer Speech and Language, 13:359–394. Joshua T. Goodman. 2001. A bit of progress in lan- guage modeling. Computer Speech and Language, 15:403–434. Slava ... 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...

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

... or even trillions of English words, huge language models are built in a distributed man- ner (Zhang et al., 2006; Brants et al., 2007). Such language models yield better translation results but at ... explore a dependency language model to improve translation quality. To some ex- tent, these syntactically-informed language models are consistent with syntax-based translation models in capturing ... integrate backward n-grams and mu- tual information (MI) triggers into language models in SMT. In conventional n-gram language models, we look at the preceding n − 1 words when calculating the probability...

Ngày tải lên: 07/03/2014, 22:20

... (lossless) lan- guages models and our randomized language model. Note that the standard practice of measuring per- plexity is not meaningful here since (1) for efficient computation, the language model ... 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 ... pruning of back- off language models. In Proc. DARPA Broadcast News Transcription and Understanding Workshop, pages 270–274. D. Talbot and M. Osborne. 2007a. Randomised language modelling for...

Ngày tải lên: 08/03/2014, 01:20

... . Class-based models. In many applications, it is nat- ural and convenient to construct class-based language models, that is models based on classes of words (Brown et al., 1992). Such models are ... the classical definitions of - gram language models and several smoothing techniques commonly used. We then describe a natural representa- tion of -gram language models using failure transitions. This ... experi- mental results demonstrating its efficiency. Representation of language models by WFAs. Clas- sical -gram language models admit a natural representa- tion by WFAs in which each state encodes...

Ngày tải lên: 08/03/2014, 04:22

... Hoang. 2007. Factored translation models. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), ... Monz. 2011. Statistical Machine Translation with Local Language Models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 869–879, Edinburgh, Scotland, ... (Tillmann, 2004; Koehn et al., 2005), two word-based language models, distortion, word and phrase penalties. The translation and re- ordering models are obtained by combining mod- els independently...

Ngày tải lên: 08/03/2014, 21:20

... Computational Linguistics Deciphering Foreign Language by Combining Language Models and Context Vectors Malte Nuhn and Arne Mauser ∗ and Hermann Ney Human Language Technology and Pattern Recognition ... to universally communi- cate in all languages. In these visions, even previ- ously unknown languages can be learned automati- cally from analyzing foreign language input. In this work, we attempt ... to learn statistical trans- lation models from only monolingual data in the source and target language. The reasoning behind this idea is that the elements of languages share sta- tistical similarities...

Ngày tải lên: 16/03/2014, 19:20

... directions: generative factored language model, discriminative language models, online passive-aggressive learning and confidence-weighted learning. Generative factored language models are pro- posed by ... Introduction Language Models (LMs) are key components in most statistical machine translation systems, where they play a crucial role in promoting output fluency. Standard n-gram generative language models have ... learning language models, we use confidence-weighted learning (Dredze et al., 2008), an extension of the perceptron-based on- line learning used in previous work on discrimi- native language models. ...

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