nested pitmanyor language modeling

... accurate word n-gram language model directly from characters of arbitrary language, without any “word” indications. 1 Introduction “Word” is no trivial concept in many languages. Asian languages such ... by the character HPYLM according to (4). This language model, which we call Nested Pitman-Yor Language Model (NPYLM) hereafter, is the hierarchical language model shown in Fig- ure 2, where the ... describes how to accomplish this by constructing a nested HPYLM of words and characters, with the associ- ated inference algorithm. 3 Nested Pitman-Yor Language Model Thus far we have assumed that...

Ngày tải lên: 17/03/2014, 01:20

... a pre- liminary machine translation reranking experiment. 2 Treelet Language Modeling The common denominator of most n-gram language models is that they assign probabilities roughly ac- cording ... top-down parsing and language modeling. Computational Linguistics. Ming Tan, Wenli Zhou, Lei Zheng, and Shaojun Wang. 2011. A large scale distributed syntactic, semantic and lexical language model ... dependencies for structured language modeling. In Proceedings of the Association for Computational Linguistics. Association for Com- putational Linguistics. Ying Zhang. 2009. Structured language models for...

Ngày tải lên: 19/02/2014, 19:20

... 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 ... and M. Fried-Oken. 2010. Scanning methods and language modeling for binary switch typing. In Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Pro- cessing for Assistive Technologies, ... confusing. Finally, language model integration with RSVP is relatively straightforward, as we shall demonstrate. See Roark et al. (2010) for methods integrating language modeling into grid scanning. 2...

Ngày tải lên: 20/02/2014, 05:20

... Computer Processing of Oriental Languages, 12(1):99–121. Jianfeng Gao, Joshua Goodman, Mingjing Li, and Kai- Fu Lee. 2002. Toward a unified approach to statis- tical language modeling for Chinese. ACM ... LVCSR. Comp. Speech Lang., 22(4):360–373. Ronald Rosenfeld. 2000. Two decades of statistical language modeling: Where do we go from here? Proceeding of IEEE, 88(8):1270–1278. George Saon and Mukund ... and Lin-Shan Lee. 1998. Statistics-based segment pat- tern lexicon: A new direction for chinese language modeling. In ICASSP, pages 169–172. 763 4.4 Application: Character-based Spoken Document Indexing...

Ngày tải lên: 20/02/2014, 07:20

... 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 ... string w alone. 2 In this sense, the scope of this paper is limited to the language modeling problem. As one example, the language modeling features might take into account n-grams, for example through ... Discriminative language modeling with condi- tional random fields and the perceptron algorithm. In Proc. ACL. Brian Roark, Murat Saraclar, and Michael Collins. 2005. Dis- criminative n-gram language modeling. ...

Ngày tải lên: 20/02/2014, 15:20

... Language, 8:1–38. B. Roark, M. Saraclar, and M. Collins. 2007. Discrimi- native n-gram Language Modeling. Computer, Speech and Language, 21:373–392. R. Rosenfeld. 1994. Adaptive Statistical Language ... Entropy Approach to Adaptive Statistical Language Modeling. Computer, Speech and Language, 10:187–228. A. Stolcke. 2002. SRILM – An Extensible Language Modeling Toolkit. In Proceedings of the ... Hierarchical Distributed Language Model. In Advances in Neural Information Processing Systems 21. H. Ney, U. Essen, and R. Kneser. 1994. On Structur- ing Probabilistic Dependences in Stochastic Language Modeling. ...

Ngày tải lên: 07/03/2014, 22:20

... for Statistical Language Modeling via Substructure Sharing and Uptraining Ariya Rastrow, Mark Dredze, Sanjeev Khudanpur Human Language Technology Center of Excellence Center for Language and Speech ... tech- niques for exploiting syntactic, semantic and colloca- tional dependencies in language modeling. Computer Speech and Language, pages 355–372. S. K ¨ ubler, R. McDonald, and J. Nivre. 2009. Depen- dency ... ICASSP. Brian Roark, Murat Saraclar, and Michael Collins. 2007. Discriminative n-gram language modeling. Computer Speech & Language, 21(2). K. Sagae and A. Lavie. 2006. A best-first probabilis- tic shift-reduce...

Ngày tải lên: 16/03/2014, 19:20

... relationship, much like traditional language models, using con- ditional probability distributions. Unlike tradi- tional language models, however, our grounded language models condition the probability ... for improving speech recognition in video. The method uses grounded language modeling, an extension of tradition lan- guage modeling in which the probability of a word is conditioned not only ... work, we will examine the ability of grounded language models to improve perform- ance for other natural language tasks that exploit text based language models, such as Machine Translation....

Ngày tải lên: 17/03/2014, 02:20

... dependency and predictive clustering for language modeling. In EMNLP 2002. Gao. J., Yu, H., Yuan, W., and Xu, P. 2005. Minimum sample risk methods for language modeling. In HLT/EMNLP 2005. Osborne, ... Statistics, 9(2): 319-337. Roark, Brian, Murat Saraclar and Michael Collins. 2004. Corrective language modeling for large vo- cabulary ASR with the perceptron algorithm. In ICASSP 2004. Schapire, ... July 2006. c 2006 Association for Computational Linguistics Approximation Lasso Methods for Language Modeling Jianfeng Gao Microsoft Research One Microsoft Way Redmond WA 98052 USA jfgao@microsoft.com...

Ngày tải lên: 17/03/2014, 04:20

... efforts have been made to utilize linguistic structure in language modeling, which for practical reasons is still dominated by trigram-based language models. There are two major obstacles to successfully ... structure acquisition. One basic approach to using linguistic structure for language modeling is to extend the conventional language model P(W) to P(W, T), where T is a parse tree of W. The extended ... headwords in phrases headed by compounds. Unsupervised Learning of Dependency Structure for Language Modeling Jianfeng Gao Microsoft Research, Asia 49 Zhichun Road, Haidian District Beijing...

Ngày tải lên: 17/03/2014, 06:20

... size is approximately 120k words. 3 Optimizing Document Clustering for Language Modeling For the purpose of language modeling, the topic la- bels assigned to a document or segment of a doc- ... contribution 172 Dynamic Nonlocal Language Modeling via Hierarchical Topic-Based Adaptation Radu Florian and David Yarowsky Computer Science Department and Center for Language and Speech Processing, ... smooth language models are computed for each node n different than the root by three-way interpolating between the m-gram language model in the parent parent(n), the (m - 1)-gram smooth language...

Ngày tải lên: 17/03/2014, 07:20

... 2000. Statisti- cal language modeling with a class-based n-multigram model. Computer Speech & Language, 14(3):261– 279. Pierre Dupont and Ronald Rosenfeld. 1997. Lattice based language models. ... classes in language modeling Hinrich Sch ¨ utze Institute for NLP University of Stuttgart Germany Abstract Building on earlier work that integrates dif- ferent factors in language modeling, we ... Introduction Language models, probability distributions over strings of words, are fundamental to many ap- plications in natural language processing. The main challenge in language modeling is...

Ngày tải lên: 23/03/2014, 16:20

... language model is utilized to capture the term dependence. A language model is a probability distribution that captures the statistical regulari- ties of natural language use. In a language ... co-occurrence with the language modeling framework for IR. In our approach, we propose bigram and biterm models to capture the term dependence in centroid vector. Applying language modeling for the ... method using language model for passage retrieval for the factoid QA. They trained two language models, in which one was the question-topic language model and the other was passage language model....

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