extensions of recurrent neural network language model
Tài liệu Handbook of Neural Network Signal Processing P2 docx
... using neural networks. In the first part, in-depth surveys of recent progress of neural network computingparadigms are presented. Part One consists of five chapters:• Chapter 1: Introduction to Neural ... extension of the conventional PCA.• Chapter 9: Applications of Artificial Neural Networks to Time Series Prediction.In this chapter, Liao, Moody, and Wu provide a technical overview of neural network approaches ... important application of artificial neural networks.In fact, a majority of neural network applications can be categorized as solving complex patternclassification problems. In the area of signal processing,...
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Tài liệu Handbook of Neural Network Signal Processing P1 ppt
... contents of this book.1.2 Artificial Neural Network (ANN) Models — An Overview1.2.1 Basic Neural Network ComponentsA neural network is a general mathematical computing paradigm that models the ... graph consists of nodes (in the case of a neural network, neurons, as wellas external inputs) and directed arcs (in the case of a neural network, synaptic links).The topology of the graph can ... a neural network with cyclic topology contains at least one cycle formed by directedarcs. Such a neural network is also known as a recurrent network. Due to the feedback loop,a recurrent network...
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Tài liệu Evolving the neural network model for forecasting air pollution time series pdf
... and comparison of models. Journal of GeophysicalResearch 90 (C5), 8995–9005.Yao, X., 1999. Evolving artificial neural networks. Proceedings of theIEEE Transactions on Neural Networks 87 (9), ... 15)Generation150Selection of parentsusing stochasticuniversal samplingCompetition of populations was notutilisedMigration within the interval of 15 gen. andrate of 0.1Reinsertion of offsprings with ... stated theevolving of model inputs and high-level architectureitself could not improve the performances of the modelssignificantly. However, more robust and reasonablemodels were produced,...
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Tài liệu Báo cáo khoa học: "The impact of language models and loss functions on repair disfluency detection" pptx
... the f-score outperform state -of- the-art models re-709mary corpus for our model. The language model part of the noisy channel model already uses a bi-gram language model based on Switchboard, ... detail.5.2 Language Model Informally, the task of language model component of the noisy channel model is to assess fluency of the sentence with disfluency removed. Ideally wewould like to have a model ... of a variety of language models trained from text or speech corpora of vari-ous genres and sizes. The largest available language models are based on written text: we investigate theeffect of...
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Tài liệu Báo cáo khoa học: "An Empirical Investigation of Discounting in Cross-Domain Language Models" ppt
... Estimating Probabilities of EnglishBigrams. Computer Speech & Language, 5(1):19–54.Joshua Goodman. 2001. A Bit of Progress in Language Modeling. Computer Speech & Language, 15(4):403–434.Bo-June ... ImprovedBacking-off for M-Gram Language Modeling. In Pro-ceedings of International Conference on Acoustics,Speech, and Signal Processing.Robert C. Moore and William Lewis. 2010. Intelligentselection of language ... ktrain(w) denote the number of occurrences of w in the training corpus, and ktest(w)denote the number of occurrences of w in the testcorpus. We define the empirical discount of w to bed(w) = ktrain(w)...
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Tài liệu Báo cáo khoa học: "Web augmentation of language models for continuous speech recognition of SMS text messages" docx
... empiricalstudy of smoothing techniques for language model- ing. Computer Speech and Language, 13:359–394.Joshua T. Goodman. 2001. A bit of progress in lan-guage modeling. Computer Speech and Language, 15:403–434.Slava ... FSTs of differ-ent sizes. The FSTs contain the acoustic models, language model and lexicon, but the LM makes upfor most of the size. The availability of data variesfor the different languages, ... size of the web mixtureLM is limited to the size of the baselinein-domain LM.1 IntroductionAn automatic speech recognition (ASR) systemconsists of acoustic models of speech sounds and of a...
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Báo cáo " Advantages and disadvantages of using computer network technology in language teaching " pptx
... out because of technical problems in terms of the speed of the network and the reliability of the software. Another challenge to the use and implementation of computer- assisted language learning ... disadvantages of using computer network technology in language teaching Vu Tuong Vi(*) (*) MA., Department of English-American Language and Culture, College of Foreign Languages - VNU. ... of second/foreign language. Indeed, the use of the Internet and the World Wide Web in second and foreign language instruction has been increasingly recognized. A number of applications of...
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Báo cáo khoa học: "Intelligent Selection of Language Model Training Data" ppt
... according to a language model trained on I, of a text segment s drawn fromN. Let HN(s) be the per-word cross-entropy of saccording to a language model trained on a ran-dom sample of N. We partition ... domain-specific and non-domain-specifc language models, for each sentence of the textsource used to produce the latter language model. We show that this produces better language models, trained on less data, ... for each of these modifed language models is compared to that of the orig-inal version of the model in Table 2. It can beseen that adjusting the vocabulary in this way, sothat all models are...
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Báo cáo khoa học: "The use of formal language models in the typology of the morphology of Amerindian languages" potx
... LinguisticsThe use of formal language models in the typology of the morphology of Amerindian languagesAndr´es Osvaldo PortaUniversidad de Buenos Aireshugporta@yahoo.com.arAbstractThe aim of this ... somepreliminary results of an investigation incourse on the typology of the morphol-ogy of the native South American lan-guages from the point of view of the for-mal language theory. With this ... examples of de-scriptions of two Aboriginal languages fi-nite verb forms morphology: ArgentineanQuechua (quichua santiague˜no) and Toba.The description of the morphology of thefinite verb forms of...
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Báo cáo khoa học: "Automatic Acquisition of Language Model based on Head-Dependent Relation between Words" pdf
... i i i ! (DEP model) o (TRI model) "*' rT I I I I I I 200 400 600 800 1000 1200 1400 1600 No. of training sentences Figure 8: Model size Related to the size of model, however, ... more useful than the naive word sequences of n-gram, for language modeling. We are planning to experiment the perfor- mance of the proposed language model for large corpus, for various domains, ... Based n-gram Models of Natural Language& quot;. Computational Linguistics, 18(4):467-480. C. Chang and C. Chen. 1996. "Application Is- sues of SA-class Bigram Language Models"....
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Báo cáo khoa học: "Combining a Statistical Language Model with Logistic Regression to Predict the Lexical and Syntactic Difficulty of Texts for FFL" potx
... astatistical language model and a measure of tensedifficulty.4.1 The language model The lexical difficulty of a text is quite an elaboratephenomenon to parameterise. The logistic regres-sion models ... factthat the MLR model multiplies the number of pa-rameters by J − 1 compared to the PO model. Because of this, they recommend using the PO model. 6 Implementation of the modelsHaving covered ... presented a variation of amultinomial naive Bayesian classifier they calledthe “Smoothed Unigram” model. We retainedfrom their work the use of language models in-stead of word lists to measure...
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Báo cáo khoa học: "Confidence-Weighted Learning of Factored Discriminative Language Models" pptx
... discriminative lan-guage models. First, we introduced the idea of us-ing factored features in the discriminative language modeling framework. Factored features allow the language model to capture linguistic ... generative language modelshave been extended in several ways. Generativefactored language models (Bilmes and Kirchhoff,2003) represent each token by multiple factors –such as part -of- speech, ... a tri-gram generative language model with Kneser-Neysmoothing. We then obtain training data for the dis-criminative language model as follows. We take arandom subset of the parallel training...
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Báo cáo khoa học: "Grounded Language Modeling for Automatic Speech Recognition of Sports Video" doc
... theoretic measure of how well a model predicts a held out test set. We use perplexity to compare our grounded language model to two baseline language models: a lan-guage model generated from ... features. We model this relationship, much like traditional language models, using con-ditional probability distributions. Unlike tradi-tional language models, however, our grounded language models ... three different lan-guage models on a held out test set of baseball high-lights (12,626 words). We compare the grounded language model to two text based language models: one trained on the...
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- recurrent neural network based language model
- recurrent neural network language model
- recurrent neural network language model toolkit
- extensions of recurrent neural network based language model
- recurrent neural network based language model bibtex
- recurrent neural network based language modeling in meeting recognition
- towards recurrent neural network language models with linguistic and contextual features
- context dependent recurrent neural network language model
- rnnlm recurrent neural network language modeling toolkit
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