N-gram based language model
Improvements to the Bayesian Topic N Gram Models
... is language model adapta- tion, which has been studied mainly in the area of speech ...an n-gram model p(w|h) and a topic model ...product model of these two mod- els, ...
11
Bi Gram based Probabilistic Language Model for Template Messaging
... in N- Gram Probability Estimation phase giving Probability Distribution Tables ...translation. N-Gram Decoder uses these tables to find the most probable translation for the input ...system ...
8
Less is More: Significance Based N gram Selection for Smaller, Better Language Models
... training N-gram language models has shown the utility of models of higher or- der than just ...in model size resulting from applying stan- dard methods at higher ...significance-based ...
10
FLOW: A First Language Oriented Writing Assistant System
... disambiguation model to benefit translation candidates selection in machine ...(WSD) model into phrase-based statistical machine ...their model directly disambiguates between all phrasal ...
6
Chinese Spelling Check System Based on N gram Model
... yet. N-gram language modeling (LM) is widely used in CSC, since its simplicity and ...a model based on joint bi-gram and tri- gram LM and Chinese word ...the ...
9
An Unsupervised Parameter Estimation Algorithm for a Generative Dependency N gram Language Model
... a language model based on a generative dependency structure for sen- ...the model is the probability of a dependency N-gram, which is composed of lexical words with four kinds of ...
9
Recurrent neural network based language model for large vocabulary continuous Tamil language speech recognition system
... the N-gram language model uses the last (n-1) words to compute the likelihood of the current ...bigram language model uses the previous one word to predict the next word ...
6
Letter N Gram based Input Encoding for Continuous Space Language Models
... 4-gram language model was trained on the target side of the parallel data using the SRILM toolkit from Stolcke ...bilingual language model as described in Niehues et ...
10
LIMSI@WMT’16: Machine Translation of News
... 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 ...
7
Statistical Input Method based on a Phrase Class n gram Model
... other language model improvement. We saw two kinds of n-gram models so far: a word n-gram model and a class n-gram ...last n − 1 ...Actually, ...
14
Federated Learning of N Gram Language Models
... and language mod- els (Ouyang et ...the language model to discriminate between viable ...the language models are typi- cally based on n-grams and do not exceed ten ...A ...
10
Abbreviated text input using language modeling.
... natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by taking advantage of the informational redundancy in natural ...been based on the ...
20
Modeling of term distance and term occurrence information for improving n gram language model performance
... TO model is closely related to the trigger language model (Rosenfeld 1996), as the prediction of the target-word (the triggered word) is based on the presence of a history-word (the ...trigger ...
5
N gram Weighting: Reducing Training Data Mismatch in Cross Domain Language Model Estimation
... data, language models are often constructed by interpolating component models trained from partially matched ...the n- grams from such corpora may not be of equal relevance to the target domain, we propose ...
10
Semi Supervised Modeling for Prenominal Modifier Ordering
... an n-gram language model and a hidden Markov model (HMM) con- structed using expectation maximization (EM) with several recent ordering approaches, and demonstrate superior performance ...
6
N gram language models for massively parallel devices
... of N-gram language models is a computa- tional ...first language model de- signed for such hardware, using B-trees to maximize data parallelism and minimize memory footprint and ...CPU- ...
10
Predicting Sentences using N Gram Language Models
... instance- based learning method achieves the highest max- imum recall (whenever this method casts a con- jecture, the entire remainder of the sentence is predicted—at a low precision), but for nearly all recall ...
8
A Large Scale Distributed Syntactic, Semantic and Lexical Language Model for Machine Translation
... 5-gram/2-SLM+2- gram/4-SLM+5-gram/PLSA language model gives ...when n-gram is used to re-rank the N -best list, how- ever, the BLEU score becomes significantly ...
10
Multi Class Composite N gram Language Model for Spoken Language Processing Using Multiple Word Clusters
... Composite N-grams in perplexity under the same conditions as the Multi-Class N-grams stated in the previ- ous ...10 based on a preliminary ...
8
Distributed Representations of Words and Documents for Discriminating Similar Languages
... approach based on distances with language prototypes to determine the language group, and next we clas- sify the language using the continuous Skip-gram model to generate ...
6