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Text Classification: An Advanced Tutorial

Text Classification: An Advanced Tutorial

Text Classification: An Advanced Tutorial. William W. Cohen Machine Learning Department, CMU. Outline. Part I: the basics What is text classification? Why do it? Representing text for classification A simple, fast generative method Some simple, fast discriminative methods

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1.14k views • 64 slides
Text Correction using Domain Dependent Bigram Models from Web Crawls

Text Correction using Domain Dependent Bigram Models from Web Crawls

Text Correction using Domain Dependent Bigram Models from Web Crawls. Christoph Ringlstetter, Max Hadersbeck, Klaus U. Schulz, and Stoyan Mihov. Two recent goals of text correction. Two recent goals of text correction. Use of powerful language models

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587 views • 47 slides
Outline

Outline

Outline. Introduction Background Distributed Database Design Database Integration Schema Matching Schema Mapping Semantic Data Control Distributed Query Processing Multimedia Query Processing Distributed Transaction Management Data Replication Parallel Database Systems

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589 views • 31 slides
WordStat & Yoshikoder

WordStat & Yoshikoder

WordStat & Yoshikoder. T.M. & M.S. WordStat. About WordStat. Must be run as part of SimStat Designed to process text such as open ended responses, journal articles, electronic communications, etc. Standard dictionaries are lacking, it is fairly easy to build your own dictionary

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283 views • 26 slides
Parallel Universes: MMOG and RL

Parallel Universes: MMOG and RL

Parallel Universes: MMOG and RL. Technology Symposium McGill University, Montreal June 9, 2006 Jane Foo Contact Info: Jane.Foo@senecac.on.ca http://people.senecac.on.ca/jane.foo Phone: 416-491-5050 x2011 IM: senlibjane (Yahoo), Jane.Foo@senecac.on.ca (MSN). Introduction.

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485 views • 25 slides
Chapter 5 Probabilistic models fo pronunciation and spelling

Chapter 5 Probabilistic models fo pronunciation and spelling

Chapter 5 Probabilistic models fo pronunciation and spelling. Xiaomeng Su 6 November. Main points. This chapter discusses the problem of detecting and correcting spelling errors.

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1.3k views • 21 slides
Sentiment Lexicon Creation from Lexical Resources

Sentiment Lexicon Creation from Lexical Resources

Sentiment Lexicon Creation from Lexical Resources. Outline. Introduction Sentiment Lexicon Creation Framework Performance Conclusions Future Work. Introduction (1). The Web offers an overwhelming amount of textual data, containing traces of sentiment

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422 views • 19 slides
Generating Word Lists

Generating Word Lists

Generating Word Lists. Why would you present word lists in experiments?. In developing word lists, and in psycholinguistic experiments in general, the careful construction of your stimuli is absolutely CRUCIAL. What to avoid? CONFOUNDS.

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82 views • 5 slides

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