... new method eliminatingmost of the gap between Kneser-Ney andthose methods.1 IntroductionStatistical languagemodels are potentially usefulfor any language technology task that producesnatural -language ... currently be the best approach when language models based on ordinary counts are desired.ReferencesChen, Stanley F., and Joshua Goodman. 1998.An empirical study of smoothing techniques for language ... a given context cannot be increased justby discounting observed counts, as long as all N- grams with the same count receive the same dis-count. Interpolation models address quantizationerror...
... Eachnode in the tree encodes a word, and paths in thetree correspond to n- grams in the collection. Triesensure that each n- gram prefix is represented onlyonce, and are very efficient when n- grams ... Methods in Natural Language Processing.Marcello Federico and Mauro Cettolo. 2007. Efficienthandling of n- gramlanguagemodels for statistical ma-chine translation. In Proceedings of the Second Work-shop ... Catalog Number LDC2006T13.Thorsten Brants, Ashok C. Popat, Peng Xu, Franz J. Och,and Jeffrey Dean. 2007. Large languagemodels inmachine translation. In Proceedings of the Conferenceon Empirical...
... Compressingtrigram languagemodels with Golomb coding. InProc. of EMNLP-CoNLL 2007.O. Delpratt, N. Rahman, and R. Raman. 2006. Engi-neering the LOUDS succinct tree representation. InProc. ... N- gram counts. By using 8-bit floating pointquantization1, N -gram languagemodels are com-pressed into 10 GB, which is comparable to a lossyrepresentation (Talbot and Brants, 2008).2 N -gram ... N -gram com-pression tasks achieved a significant com-pression rate without any loss.1 IntroductionThere has been an increase in available N -gram data and a large amount of web-scaled N- gram data...
... language mod-els in machine translation. In Proceedings of the2007 Joint Conference on Empirical Methods in Nat-ural Language Processing and Computational Natu-ral Language Learning (EMNLP-CoNLL), ... fromtraining data to enhance conventional n- gram lan-guage models and extend their ability to capturericher contexts and long-distance dependencies. Inparticular, we integrate backward n- grams and ... information (MI) triggers into language models in SMT.In conventional n- gramlanguage models, we lookat the preceding n − 1 words when calculating theprobability of the current word. We henceforth...
... Workshopon Natural Language Generation.Natalia N. Modjeska, Katja Markert, and Malvina Nis-sim. 2003. Using the Web in machine learning forother-anaphora resolution. In EMNLP.Preslav Nakov and ... Search enginestatistics beyond the n- gram: Application to nouncompound bracketing. In CoNLL.Preslav Ivanov Nakov. 2007. Using the Web as an Im-plicit Training Set: Application to Noun CompoundSyntax ... Large-scalesupervised models for noun phrase bracketing. InPACLING.Xiaofeng Yang, Jian Su, and Chew Lim Tan. 2005.Improving pronoun resolution using statistics-basedsemantic compatibility information. In ACL.874be...
... 2009.Quadratic-time dependency parsing for machine trans-lation. In Proceedings of the Joint Conference of the47th Annual Meeting of the ACL and the 4th Interna-tional Joint Conference on Natural Language ... Language modelingwith tree substitution grammars. In NIPS workshop onGrammar Induction, Representation of Language, and Language Learning.Arjen Poutsma. 1998. Data-oriented translation. InNinth ... Henderson. 2004. Lookahead in deterministicleft-corner parsing. In Proceedings of the Workshopon Incremental Parsing: Bringing Engineering andCognition Together, pages 26–33.Liang Huang and...
... ofTraining Corpus Size on Classifier Performance forNatural Language Processing. In Proceedings of theFirst International Conference on Human Language Technology Research.Eugene Charniak and Mark ... impact of languagemodels and loss functions on repair disfluencydetectionSimon Zwarts and Mark JohnsonCentre for Language TechnologyMacquarie University{simon.zwarts|mark.johnson|}@mq.edu.auAbstractUnrehearsed ... 2002. SRILM - An Extensible Lan-guage Modeling Toolkit. In Proceedings of the Inter-national Conference on Spoken Language Processing,pages 901–904.Qi Zhang, Fuliang Weng, and Zhe Feng. 2006....
... sincefrequent n- grams will appear in more of the re-jected sentences, and nonuniform discounting over n- grams of each count, since the sentences are cho-sen according to a likelihood criterion. Althoughwe ... median (1.0) are very different.constant function of n- gram counts. In Figure 2, weinvestigate the second assumption, namely that thedistribution over discounts for a given n- gram countis ... diver-gence yields higher discounting. One explanationfor the remaining variance is that the trigram dis-count curve depends on the difference between thenumber of bigram types in the train and...
... text and “adult” text. This resultedin 12 LM perplexity features per article based ontrigram, bigram and unigram LMs trained on Bri-tannica (adult), Britannica Elementary, CNN (adult)and CNN ... based on the observedfrequency in a training corpus and smoothed usingmodified Kneser-Ney smoothing (Chen and Good-man, 1999). We used the SRI Language ModelingToolkit (Stolcke, 2002) for language ... EngineeringUniversity of WashingtonSeattle, WA 98195-2500mo@ee.washington.eduAbstractReading proficiency is a fundamen-tal component of language competency.However, finding topical texts at an...
... ∑∑∑∑∈∈∈∈=}{),(}{),()()()(CandidatesCnCSngramCandidatesCnCSngramclipngramCountngramCountnP where Count(ngram) is the number of n- gram counts, and Countclip(ngram) is the maximum number of co-occurrences of ngram ... ),()()()(ferencesRnRSngramferencesRnRSngramclipngramCountngramCountnR where, as before, Count(ngram) is the number of n- gram counts, and Countclip(ngram) is the maximum number of co-occurrences of ngram in the reference answer and its corresponding ... using ST and eliminating the unigrams found in SW. We therefore define a recall score as: ∑∑∑∑∈∈∈∈=}{Re ),(}{Re ),()()()(ferencesRnRSngramferencesRnRSngramclipngramCountngramCountnR...
... Contextual Information and Spe-cific LanguageModels for Spoken Language Un-derstanding. In Proceedings of SPECOM’97, Cluj-Napoca, Romania, pp. 51–56.Bangalore S. and Johnston M. 2004. Balancing ... of generating Nu-ance recognition grammars from the interpretationgrammar and the possibility of generating corporafrom the grammars. The interpretation grammarfor the domain, written in GF, ... fallingoff in in-grammar performance. It is interest-ing that the SLM that only models the grammar(MP3GFLM), although being more robust and giv-ing a significant reduction in WER rate, does notdegrade...
... chunk the in-domain textinto n- gram islands” consisting of only contentwords and excluding frequently occurring stopwords. An island such as “stock fund portfolio” isthen extended by adding ... On growing and pruning 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 ... documents containing an x % relevant n- gram, x % are relevant. When the n- grams havebeen ranked into a presumed order of relevance,we decide that the most relevant n- gram is 100 %relevant and...
... two native Argentinean languagessive power equal to the noncyclic componentsof generative grammmars representing the mor-phophonology of natural languages. However,these works make no considerations ... regular grammars for modeling agglutinationin this language, but first we will present the for-mer class of languages and its acceptor automata.3.1 Linear context free languages andtwo-taped nondeterministic ... natural representa-tion in terms of linear context-free languages.2 Quichua Santiague˜noThe quichua santiague˜no is a language of theQuechua language family. It is spoken in the San-tiago del...
... Melbourne, Australia.R. Kneser and H. Ney. 1995. Improved backing-off for n- gramlanguage modeling. In Acoustics, Speech, andSignal Processing, 1995. ICASSP-95., 1995 Interna-tional Conference on, ... corpus of (Brants and Franz, 2006), presenta major challenge. The languagemodels built fromthese sets cannot fit in memory, hence efficient ac-cessing of the N- gram frequencies becomes an is-sue. ... definition, eachinternal node except the root can have any number ofkeys in the range [v, 2v], and the root must have atleast one key. Finally, an internal node with k keyshas k + 1 children.4.2...
... Encoding an n -gram s value in the array.function for a given set of n- grams is a significantchallenge described in the following sections.3.3 Encoding n- grams in the modelAll addresses in ... IEEE International Conference onAcoustics, Speech and Signal Processing (ICASSP)2007, Hawaii, USA.J. Goodman and J. Gao. 2000. Language model size re-duction by pruning and clustering. In ICSLP’00, ... to finding an orderedmatching in a bipartite graph whose LHS nodes cor-respond to n- grams in S and RHS nodes correspondto locations in A. The graph initially contains edgesfrom each n- gram to...