PPT-SI485i : NLP

Set 3 Language Models Fall 2012 Chambers Language Modeling Which sentence is most likely most probable I saw this dog running across the street Saw dog this I running across street the Why. Set 8. PCFGs and the CKY Algorithm. PCFGs. We saw how CFGs can model English (sort of). Probabilistic. . CFGs put weights on the production rules. NP -> DET NN . with probability 0.34. NP -> . Set 6. Sentiment and Opinions. It's about finding out what people think.... Can be big business…. Someone who wants to buy a camera. Looks for reviews online. Someone who just bought a camera. Writes reviews online. Christopher Manning. Stanford University. Digital Humanities 2011. http://nlp.stanford.edu/~manning/courses/DigitalHumanities. /. . Commencement 2010. My humanities qualifications. B.A. (. Hons. ), Australian National University. SI485i : NLP Set 3 Fall 2012 : Chambers Language Modeling • Which sentence is most likely (most probable)? I saw this dog running across the street. Saw dog this I running across street the. Wh Detecting Questions in Micro-text. Kyle Dent <. kdent@parc.com. >. Original Question: Do people use Twitter to ask questions?. First Answer: Of course.. But to what extent and what kinds of questions?. Morphology and the Lexicon. Mental Lexicon. What is the meaning of cat? Its pronunciation? Part of speech?. What is the meaning of . wug. ?. What is the meaning of . cluvious. ?. Compare . traftful. and . the almighty……….. . NLP. . DR.Eman. . Diab. introduction. introduction. The word . Neuro. linguistic programming can be broken down to three distinct words: . Class Logistics. Quiz. Where is this quote from?. Dave Bowman. : Open the pod bay doors, HAL.. HAL. : I’m sorry Dave. I’m afraid I can’t do that.. Quiz Answer. “2001: A Space Odyssey” . 1968 film by Stanley Kubrick . In the Context of the ETC Project. SAW Intro. Wiki. Users collaboratively develop and organize content. Information Analysis is up to the user. Goal: . “Self-aware wiki that can develop and organize its content. Language Annotation, Modeling, and Processing . Toolkit (CLAMP) . Hua Xu. School of Biomedical Informatics, . University . of Texas Health Science Center at Houston . 1. The Transportability Problem of . Jimmy Lin. The . iSchool. University of Maryland. Wednesday, September 2, 2009. NLP. IR. About Me. Teaching Assistant: . Melissa Egan. CLIP. About You (pre-requisites). Must be interested in NLP. Must have strong computational background. s. lides adapted from Dan . Jurafsky. and Bill . MacCartney. Three. levels of . m. eaning. Lexical . Semantics (words). Sentential / Compositional / Formal Semantics. Discourse . or Pragmatics. m. eaning + context + world knowledge. Jiho . Han. Ronny (. Dowon. ) . Ko. Objective:. automatically generate the summary of review extracting the strength/weakness of the product. Use NLP techniques to predict ratings. Similar to sentimental analysis. by Hua Xu. Recent Activities. ETL tool development. Note Type Normalization. COVID-19 lab test normalization . A Potential ETL Workflow for NLP. Note. Note_NLP. Measure-. ment. Condition. Procedure. Drug.