Computer Speech and Language[removed], 187–228 A maximum entropy approach to adaptive statistical language modelling Ronald Rosenfeld Computer Science Department, Carnegie Mellon University, Pittsburgh,

Source URL: www.eecs.yorku.ca

• Statistical theory
• Information theory
• Natural language processing
• Markov models
• Perplexity
• Computational linguistics
• Language model
• N-gram
• Entropy
• Statistics
• Probability and statistics
• Science

Reducing the Sampling Complexity of Topic Models Aaron Q. Li Amr Ahmed CMU Language Technologies

Source URL: www.sravi.org

• Markov chain Monte Carlo
• Latent Dirichlet allocation
• Monte Carlo methods
• Dirichlet process
• Perplexity
• Mixture model
• Topic model
• Concentration parameter
• Dirichlet distribution
• Statistics
• Probability and statistics
• Statistical natural language processing

LNAI[removed]Another Look at the Data Sparsity Problem

Source URL: homepages.inf.ed.ac.uk

• Natural language processing
• Speech recognition
• Corpus linguistics
• Statistical natural language processing
• N-gram
• Language model
• Perplexity
• Statistical machine translation
• Bigram
• Linguistics
• Computational linguistics
• Science

Report on the Kepler Prize Workshop on Complex Living Systems Managing Complexity, Reducing Perplexity Heidelberg, May 16-20, 2011 A workshop on "Managing Complexity, Reducing Perplexity", has been held in

Source URL: www.eurasc.org

• Germany
• Scientific modelling
• Johannes Kepler
• German Cancer Research Center
• Heidelberg
• States of Germany
• Heidelberg University

Training Finite-State Transducer Cascades with Carmel Kevin Knight, October 2009 Summary This document describes how to train arbitrary cascades of finite-state machines on end-to-end data. The Carmel toolkit implements

Source URL: www.isi.edu

Compositional Morphology for Word Representations and Language Modelling Jan A. Botha Phil Blunsom Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK

Source URL: jmlr.org

• Word-sense disambiguation
• N-gram
• Statistical machine translation
• Natural language processing
• Perplexity
• ALGOL 68
• Speech recognition
• Morphology
• Vector space model
• Computational linguistics
• Linguistics
• Science

Grounding language models in spatiotemporal context Brandon C. Roy, Soroush Vosoughi, Deb Roy The Media Lab, Massachusetts Institute of Technology Cambridge, MA[removed]removed], [removed], dkroy@med

Source URL: www.media.mit.edu

• Computational linguistics
• Linguistics
• Speech recognition
• N-gram
• Language model
• Perplexity
• Bigram
• Psycholinguistics
• Factored language model
• Statistical natural language processing
• Natural language processing
• Science

Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim1 , Prem Gopalan2 , David M. Blei2 , and Erik B. Sudderth1 1 2

Source URL: www.cs.princeton.edu

• Bayesian statistics
• Graph theory
• Statistical natural language processing
• Variational Bayesian methods
• Perplexity
• Community structure
• Degree distribution
• Pruning
• Latent Dirichlet allocation
• Statistics
• Networks
• Mathematics

Multimodal Neural Language Models Ryan Kiros Ruslan Salakhutdinov Richard Zemel Department of Computer Science, University of Toronto

Source URL: www.cs.toronto.edu

• Linguistics
• Computational neuroscience
• Perplexity
• N-gram
• Neural network
• Computational science
• Natural language processing
• Speech recognition
• Computational linguistics
• Science

Grounded Language Modeling for Automatic Speech Recognition of Sports Video Michael Fleischman Massachusetts Institute of Technology Media Laboratory [removed]

Source URL: www.media.mit.edu

• Linguistics
• Knowledge
• Closed captioning
• Natural language processing
• Perplexity
• Cognitive science
• Language model
• N-gram
• Video search engine
• Computational linguistics
• Speech recognition
• Science