'Unigram' diaporamas de présentation

Modeling Users and Content : Structured Probabilistic Representation and Scalable Online Inference Algorithms

Modeling Users and Content : Structured Probabilistic Representation and Scalable Online Inference Algorithms . Amr Ahmed Thesis Defense. The Infosphere. News Sources. Social Media. Research Publications. President Obama had an accident while playing a basketball match.

1.58k views • 146 slides

Smoothing N-gram Language Models

Smoothing N-gram Language Models. Shallow Processing Techniques for NLP Ling570 October 24, 2011. Roadmap. Comparing N-gram Models Managing Sparse Data: Smoothing Add-one smoothing Good-Turing Smoothing Interpolation Backoff Extended N-gram Models Class-based n-grams

1.95k views • 94 slides

Language Models & Smoothing

Language Models & Smoothing. Shallow Processing Techniques for NLP Ling570 October 19, 2011. Announcements. Career exploration talk: Bill McNeill Thursday (10/20): 2:30-3:30pm Thomson 135 & Online ( T reehouse URL) Treehouse meeting: Friday 10/21: 11-12 Thesis topic brainstorming

1.04k views • 82 slides

?????? : ???? Statistical Language Models for IR

?????? : ???? Statistical Language Models for IR. ChengXiang Zhai (???) Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign

984 views • 80 slides

Information theory and phonology

Information theory and phonology. John Goldsmith The University of Chicago. All the particular properties that give a language its unique phonological character can be expressed in numbers. -Nicolai Trubetzkoy . Outline.

944 views • 66 slides

Beyond Search: Statistical Topic Models for Text Analysis

Beyond Search: Statistical Topic Models for Text Analysis . ChengXiang Zhai Department of Computer Science University of Illinois at Urbana-Champaign http://www.cs.uiuc.edu/homes/czhai. Search is a means to the end of finishing a task . Information Synthesis & Analysis .

785 views • 61 slides

Lecture 6: N-gram Models and Sparse Data (Chapter 6 of Manning and Schutze, Chapter 6 of Jurafsky and Martin, and Chen

Lecture 6: N-gram Models and Sparse Data (Chapter 6 of Manning and Schutze, Chapter 6 of Jurafsky and Martin, and Chen and Goodman 1998). Wen-Hsiang Lu ( ??? ) Department of Computer Science and Information Engineering, National Cheng Kung University 2004/10/20

840 views • 57 slides

Call Routing

730 views • 57 slides

CS276A Text Retrieval and Mining

624 views • 43 slides

CS276A Text Retrieval and Mining

565 views • 43 slides

Extraction of Opinions on the Web

Extraction of Opinions on the Web. Richard Johansson. Computer Science and Engineering Department University of Trento Email : johansson@disi.unitn.it. Funded by EU FP7: LivingKnowledge and EternalS. Presentation at the LK summer school August 31, 2011. Personal Background.

510 views • 41 slides

I256: Applied Natural Language Processing

I256: Applied Natural Language Processing. Marti Hearst Sept 20, 2006. Tagging methods. Hand-coded Statistical taggers Brill (transformation-based) tagger. Type of taggers: tag.Default() tag.Regexp() tag.Affix() tag.Unigram() tag.Bigram() tag.Trigram(). Actions: tag.tag()

643 views • 39 slides

Machine Learning PoS-Taggers

281 views • 27 slides

Introduction to Machine Learning and Text Mining

328 views • 25 slides

Generating Impact-Based Summaries for Scientific Literature

340 views • 23 slides

CS 388: Natural Language Processing: N-Gram Language Models

752 views • 22 slides

CS 388: Natural Language Processing: N-Gram Language Models

575 views • 22 slides

167 views • 9 slides