[PDF] Top 20 Class Based n gram Models of Natural Language
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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] ... See full document
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Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Language models are important in natural language processing tasks such as speech recognition and statistical machine ...off n-gram language models (BNLMs) (Chen ... See full document
6
Less is More: Significance Based N gram Selection for Smaller, Better Language Models
... an N-gram probablity is not ...on language model pruning, and he also pointed out that weighted dif- ference pruning is, in fact, an approximation of relative entropy ...is based on a ... See full document
10
A Joint Source Channel Model for Machine Transliteration
... using n-gram language models (Jelinek, ...source language, say E, into an intermediate phonemic representation P, and then convert the phonemic representation into the target ... See full document
8
Letter N Gram based Input Encoding for Continuous Space Language Models
... ter n-grams will yield a good indicator for words that have the same function inside the ...letter n-gram approach to represent words in an CSLM, and compare it to the word-based CSLM ... See full document
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LIMSI @ WMT’14 Medical Translation Task
... an n-gram system and an on-the-fly phrase-based model, in a new medical translation task, through various approaches to perform do- main ...ous language models, which yield additional ... See full document
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From n gram based to CRF based Translation Models
... target language model used, if ...with n-gram-based systems subject to the same input and comparable resources, varying the order of the tuple language model, as well as with ... See full document
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Grammatical Machine Translation
... translation models has not yet been attempted in dependency-based ...realization models that can easily be trained to reflect the ordering of the reference translations in the training corpus are ... See full document
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Predicting Sentences using N Gram Language Models
... How do instance-based learning and N -gram completion compare in terms of computation time? The Viterbi beam search decoder is linear in the pre- diction length. The index-based retrieval ... See full document
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Language Identification of Short Text Segments with N-gram Models
... character-based language identification with n- gram language ...compact models that do not need word-based features, this approach is well suited for language ... See full document
8
Improvements to the Bayesian Topic N Gram Models
... is language model adapta- tion, which has been studied mainly in the area of speech ...trained models: an n-gram model p(w|h) and a topic model ...simpler models such as linear ... See full document
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Character n-Gram Embeddings to Improve RNN Language Models
... (RNN) language model that takes advantage of character ...character n-grams based on research in the field of word embedding construction (Wieting et ...character n- gram embeddings and ... See full document
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Experience with a Stack Decoder Based HMM CSR and Back Off N Gram Language Models
... Experience with a Stack Decoder Based HMM CSR and Back Off N Gram Language Models E x p e r i e n c e w i t h a S t a c k D e c o d e r B a s e d H M M C S R a n d B a c k O f f N G r a m L a n g u a[.] ... See full document
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Auto Sizing Neural Networks: With Applications to n gram Language Models
... for language modeling for a long ...character n-gram model using neural networks which they used for text ...word n-gram model and demonstrated improvements over con- ventional smoothed ... See full document
9
N gram Counts and Language Models from the Common Crawl
... sequence models built on the in-domain subset of the parallel corpus using Kneser-Ney smoothed 7-gram models and as additional factors in phrase translation mod- els (Koehn and Hoang, ...guage ... See full document
6
N gram and Neural Language Models for Discriminating Similar Languages
... character n-grams (with n=1 to 6) and word n-grams (with n=1 to ...the language group and then the specific language variant (Goutte and Leger, ... See full document
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Book Reviews: Computational Approaches to Morphology and Syntax by Brian Roark and Richard Sproat
... including n-gram models and smoothing, class-based language models, hidden Markov models (though without a formal definition), part-of-speech tagging, log-linear ... See full document
6
Statistical Input Method based on a Phrase Class n gram Model
... the language model for KKC. An n-gram model is generally used for many ...the language model, bi-gram models are often ...bi-gram models can not refer to a long ... See full document
14
Evaluation of Language Models over Croatian Newspaper Texts
... Statistical language modeling involves techniques and procedures that assign probabilities to word sequences or, said in other words, estimate the regularity of the ...statistical language models, ... See full document
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Language Models for Contextual Error Detection and Correction
... In addition to the k-NN classifier, we also run the experiments using the IGTree classifier, which is denoted IGTree in the rest of the article, which is also contained in the TiMBL distribution. IGTree is a fast, trie ... See full document
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