n-gram models
Dependency Based N Gram Models for General Purpose Sentence Realisation
... 4-gram models in all experi- ...Different n-gram models perform nearly consis- tently in all the experiments on both English and Chinese ...factored n- gram models ...
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Statistical Representation of Grammaticality Judgements: the Limits of N Gram Models
... enriched n-gram models to track grammaticality judgements for different sorts of passive sentences in ...these models by specifying scoring functions to map the log probabilities (logprobs) of ...
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Attacking Decipherment Problems Optimally with Low Order N gram Models
... We introduce a method for solving substi- tution ciphers using low-order letter n-gram models. This method enforces global con- straints using integer programming, and it guarantees that no ...
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Language Identification of Short Text Segments with N-gram Models
... with n- gram language models. Because of the compact models that do not need word-based features, this approach is well suited for language identification tasks that have dozens of languages, ...
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Distributed representation and estimation of WFST based n gram models
... 5- gram language models with different parameteri- zations for determining the model ...or n-gram frequency cutoffs were used, but for the search queries, as part of the pre- processing and ...
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Class Based n gram Models of Natural Language
... We estimate the parameters of an n-gram model by examining a sample of text, t~, which we call the training text, in a process called training.. To estimate the parameters of an n-gram m[r] ...
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Reduced n gram Models for English and Chinese Corpora
... The Mandarin News corpus from the LDC, catalog no. LDC95T13 was obtained from the People’s Daily Newspaper from 1991 to 1996 (125 million syllables); from the Xinhua News Agency from 1994 to 1996 (25 million syllables); ...
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Improvements to the Bayesian Topic N Gram Models
... language models, and ex- periments with higher-order n-grams have not yet been sufficiently studied, which we investigate in this ...all n-grams into exclusive topics, and local minima caused by the ...
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Beyond N in N gram Tagging
... it models local context but not global context, leaving long-distance syntactic relations ...Using n-gram models for n > 3 in order to incorporate global context is problematic as ...
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Evaluation of Language Models over Croatian Newspaper Texts
... language models, reviews their use in the large set of speech and language applications, explains their formal definition and shows different types of language ...of n-gram and class- based ...
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LIMSI@WMT’16: Machine Translation of News
... estimate n-gram models that use large output vocabulary, thereby making the training of large neural net- work language models feasible both for target lan- guage models (Le et ...
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Generalized Interpolation in Decision Tree LM
... Models are trained on 35M words of WSJ 94-96 from LDC2008T13. The text was converted into speech-like form, namely numbers and abbrevia- tions were verbalized, text was downcased, punc- tuation was removed, and ...
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An Empirical Comparison Between N gram and Syntactic Language Models for Word Ordering
... language models and N-gram language models have both been used in word ...between N-gram and syntactic language models on word or- der ...and N-gram mod- els ...
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Subsegmental language detection in Celtic language text
... This paper has presented a very preliminary investigation into subsegment language identification in Celtic language texts. We have proposed a model that chunks input text into segments and performs language ...
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Letter N Gram based Input Encoding for Continuous Space Language Models
... standard n-gram language models (Bengio et ...rescore n-best lists of a ma- chine translation system during tuning and testing ...smaller n- gram model (Le et ...cal ...
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Generalizing and Hybridizing Count based and Neural Language Models
... hybrid models combine the advantages of neural and n- gram models, we compare with the more standard method of training models independently and com- bining them with static ...
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LIMSI @ WMT’14 Medical Translation Task
... the n-gram translation models and target n- gram language models, 13 conventional features are combined: 4 lexicon models similar to the ones used in standard phrase-based ...
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Auto Sizing Neural Networks: With Applications to n gram Language Models
... Neural networks have proven to be highly ef- fective at many tasks in natural language. For example, neural language models and joint lan- guage/translation models improve machine trans- lation quality ...
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Resources to Examine the Quality of Word Embedding Models Trained on n Gram Data
... Google n-gram corpus remains ...extracted n-grams are contained in the Google n-gram data as well, despite its large threshold ...Google n-gram corpus ...all ...
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Grammatical Machine Translation
... also be used for generation of English translations. For in-coverage examples, the grammar specifies c- structures that differ in linear precedence of sub- trees for a given f-structure, and realizes the termi- nal yield ...
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