PPT-Language modeling: Smoothing

David Kauchak CS159 Spring 2011 some slides adapted from Jason Eisner Admin Assignment 2 out bigram language modeling Java Can work with partners Anyone looking for a partner Due Wednesday 216 but start working on it now. April, 31, 2011. Tom Buggey, Ph D. Professor/Chair of Excellence in Early Childhood. . Special . Education. The University of Tennessee Chattanooga. VSM Overview. this video is in “videos” section of this site.. June 2012, Planetary Mappers Meeting. Smoothing Contacts. What is smoothing:. Smoothing allows you to add curvature to linework making it . arcuate. instead of straight between vertices. Why you should smooth:. Smoothing Smoothing • F (smoothing) could be implemented by energy minimization • D ifferent energy functions can be used for different approaches • T he most frequent function is the References. : 1. 11.2.2, 11.3, 11.4 of Huang or . 2. 6.1- 6.8 of Becchetti, or. 3. 4.1- 4.5, 8.3 of Jelinek. Language Modeling. : providing linguistic constraints to help the selection of correct words. http://. www.youtube.com. /. watch?v. =RE4ce4mexrU. Language modeling:. Smoothing. David Kauchak. CS159 . – Fall . 2014. some slides adapted from Jason Eisner. Admin. Assignment 2 out. bigram language modeling. http://. www.youtube.com. /. watch?v. =RE4ce4mexrU. Language modeling:. Smoothing. David Kauchak. CS457 . – . Fall . 2011. some slides adapted from Jason Eisner. Admin. Assignment 2 out. bigram language modeling. Part II: Smoothing Techniques. Niranjan Balasubramanian. Slide Credits:. Chris Manning, Dan . Jurafsky. , . Mausam. Recap. A language model is something that specifies the following two quantities, for all words in the vocabulary (of a language).. Capture-Recapture. Kneser. -Ney. Additive Smoothing. https://. en.wikipedia.org/wiki/Additive_smoothing. . Laplace Smoothing. Jeffreys. Dirichlet. Prior. What’s wrong with adding one?. 10/27/2017. CyberGIS. : . A Demonstration with Flux Footprint Modeling. Michael E. Hodgson, April Hiscox, Shaowen Wang, Babak Behzad, Sara Flecher, . Kiumars. . Soltani. , Yan Liu and Anand Padmanabhan. Receptor . N-Gram Language Models ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign 1 Outline General questions to ask about a language model N-gram language models Probabilistic Language Models. Today’s goal: assign a probability to a sentence. Machine Translation:. P(. high . winds . tonite. ) > P(. large. winds . tonite. ). Spell Correction. The office is about fifteen . Probabilistic Language Models. Today’s goal: assign a probability to a sentence. Machine Translation:. P(. high . winds . tonite. ) > P(. large. winds . tonite. ). Spell Correction. The office is about fifteen . Chapter 3. (3.1-3.4). Review. Text Normalization. Why?. How computationally?. Example tasks?. 2. Rule-based vs. Probabilistic. “. But it must be recognized that the notion of “probability of a sentence” is an entirely useless one, under any known interpretation of this term.” . Jennifer Gray, M.S., CCC-SLP. Disclosure Statement. Grays Peak Speech Service, LLC. Private practice (children, teens, and adults), Early Intervention, and early education providing speech, language, and feeding services..