back-off n-gram language model
An Unsupervised Parameter Estimation Algorithm for a Generative Dependency N gram Language Model
... dency N-gram language model and the definition of the complete sets for arbitrary order, by which an unsupervised parameter estimation algorithm is ...dependency language model ...
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Modeling of term distance and term occurrence information for improving n gram language model performance
... order n -gram models, the complementary information captured by the TD and TO components reduced the per- plexity up to ...order n - gram models, ...of n -grams to model long ...
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Multi Class Composite N gram Language Model for Spoken Language Processing Using Multiple Word Clusters
... We have also evaluated Multi-Class Composite N-grams in perplexity under the same conditions as the Multi-Class N-grams stated in the previ- ous section. The Multi-Class 2-gram is used for the ...
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A Challenge Set for Advancing Language Modeling
... an N-gram language model to do this rank- ...guage model is used, and this receives the highest weight. N-gram statistics were also very effective - according to one of ...
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Learning Bilingual Word Representations by Marginalizing Alignments
... log-bilinear model are as follows. Where the original log-bilinear lan- guage model uses context words to predict the next word—this is simply the distributed extension of an n-gram ...
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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 ...
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Improvements to the Bayesian Topic N Gram Models
... the language phenomena that n-gram language model fails to capture is the topic in- formation of a given ...topic language model by Wallach (2006) in two directions: one, ...
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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 ...
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N gram Weighting: Reducing Training Data Mismatch in Cross Domain Language Model Estimation
... each n-gram count in a standard n-gram language model (LM) estimation procedure by a rel- evance factor computed via a log-linear combina- tion of n-gram ...of ...
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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 ...generative model of ...
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Unsupervised Code Switching for Multilingual Historical Document Transcription
... cal offset of each character from a common base- line. Additionally, since documents exhibit variable inking levels (where individual characters are often faded or smeared with blotched ink) the system also models the ...
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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 language ...
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Faster and Smaller N Gram Language Models
... the language model queries issued by the Joshua de- coder (Li et ...of language model queries in a cache should be effective at reducing overall language model ...given ...
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N gram language models for massively parallel devices
... natural language pro- cessing, in problems such as parsing (Canny et ...computational model rewards data parallelism, minimal branching, and minimal ac- cess to global memory, patterns ignored by many ...
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RNN language model with word clustering and class-based output layer
... Statistical language models estimate the probability of a word occurring in a given context, which plays an impor- tant role in many natural language processing applica- tions such as speech recognition, ...
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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 translation ...
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A Large Scale Distributed Syntactic, Semantic and Lexical Language Model for Machine Translation
... previous n-1 words, have been the workhorses of state-of-the-art speech recognizers and machine translators that help to resolve acoustic or foreign language ambiguities by placing higher probability on ...
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Predicting Sentences using N Gram Language Models
... a language model in order to provide a (human) translator with a list of sug- gestions (Langlais et ...a model that adapts to a user’s typing speed in or- der to achieve a better trade-off ...
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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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Enhancing Language Models in Statistical Machine Translation with Backward N grams and Mutual Information Triggers
... standard n-gram language models in captur- ing richer contexts and long-distance dependencies that go beyond the scope of forward n-gram win- ...first model is the backward ...
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