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Hinrich Schütze and Christina Lioma Lecture 13: Text Classification & Naive Bayes

Hinrich Schütze and Christina Lioma Lecture 13: Text Classification & Naive Bayes

Hinrich Schütze and Christina Lioma Lecture 13: Text Classification & Naive Bayes. Overview. Recap Text classification Naive Bayes NB theory Evaluation of TC. Overview. Recap Text classification Naive Bayes NB theory Evaluation of TC. Looking vs. Clicking. 4.

By hugh
(265 views)

Research in Information Retrieval and Management

Research in Information Retrieval and Management

Research in Information Retrieval and Management. Susan Dumais Microsoft Research. Library of Congress Feb 8, 1999. Research in IR at MS. Microsoft Research (http://research.microsoft.com) Decision Theory and Adaptive Systems Natural Language Processing MSR Cambridge User Interface

By gen
(147 views)

Text Mining: Techniques, Tools, ontologies and Shared tasks

Text Mining: Techniques, Tools, ontologies and Shared tasks

Text Mining: Techniques, Tools, ontologies and Shared tasks. Xiao Liu, with updates from Shuo Yu Spring 2019. Introduction. Text mining, also referred to as text data mining, refers to the process of deriving high quality information from text.

By lynne
(134 views)

Mixture Models for Document Clustering

Mixture Models for Document Clustering

Mixture Models for Document Clustering. Edward J. Wegman Yasmin H. Said George Mason University, College of Science October 30, 2008 University of Maryland, College Park. Outline. Overview of Text Mining Vector Space Text Models Latent Semantic Indexing Social Networks

By dayo
(199 views)

Learning the Semantic Meaning of a Concept from the Web

Learning the Semantic Meaning of a Concept from the Web

Learning the Semantic Meaning of a Concept from the Web. Yang Yu and Yun Peng May 30, 2007 yangyu1@umbc.edu, ypeng@umbc.edu. LIVING_THINGS. ANIMAL. PLANT. HUMAN. CAT. TREE. GRASS. MAN. WOMAN. ARBOR. FRUTEX. The Problem .

By annis
(130 views)

Datamining MEDLINE for Topics and Trends in Dental and Craniofacial Research

Datamining MEDLINE for Topics and Trends in Dental and Craniofacial Research

Datamining MEDLINE for Topics and Trends in Dental and Craniofacial Research . William C. Bartling, D.D.S. NIDCR/NLM Fellow in Dental Informatics Center for Biomedical Informatics University of Pittsburgh Titus K. L. Schleyer, D.M.D., Ph.D. Director, Center for Dental Informatics

By ashby
(106 views)

Semantic-based Language Models for Text Retrieval and Clustering

Semantic-based Language Models for Text Retrieval and Clustering

Semantic-based Language Models for Text Retrieval and Clustering. Xiaohua (Davis) Zhou College of Information Science & Technology Drexel University. Summary of Research Work. Publication in last three years

By shina
(222 views)

Tackling the Poor Assumptions of Naive Bayes Text Classifiers Pubished by: Jason D.M.Rennie, Lawrence Shih, Jamime Teeva

Tackling the Poor Assumptions of Naive Bayes Text Classifiers Pubished by: Jason D.M.Rennie, Lawrence Shih, Jamime Teeva

Tackling the Poor Assumptions of Naive Bayes Text Classifiers Pubished by: Jason D.M.Rennie, Lawrence Shih, Jamime Teevan, David R.Karger . Liang Lan 11/19/2007. Outline. Introduce the Multinomial Naive Bayes Model for Text Classification. The Poor Assumption of Multinomial Naive Bayes Model.

By giselle
(328 views)

STUDENT RESEARCH SYMPOSIUM 2005

STUDENT RESEARCH SYMPOSIUM 2005

STUDENT RESEARCH SYMPOSIUM 2005. Title: Strategically using Pairwise Classification to Improve Category Prediction Presenter: Pinar Donmez Advisors: Carolyn Penstein Ros é , Jaime Carbonell LTI, SCS Carnegie Mellon University. Outline. Problem Definition

By ferguson
(130 views)

Text Classification

Text Classification

Text Classification. Classification Learning (aka supervised learning). Given labelled examples of a concept (called training examples) Learn to predit the class label of new (unseen) examples

By nicki
(163 views)

TAC Summarisation System

TAC Summarisation System

TAC Summarisation System. WING Meeting 8 Jul 2011. Ziheng Lin, Praveen Bysani , Jun-Ping Ng. Outline. Introduction Methodology Experimental Results Conclusion. Introduction. TAC 2011 Guided Summarization. Summarization: guided by “importance” of facts

By shawn
(62 views)

4 th Nov, 2002.

4 th Nov, 2002.

Happy Deepavali!. 10/25. 4 th Nov, 2002. Text Classification. Classification Learning (aka supervised learning). Evaluating Classification techniques: Accuracy on test data (for various sizes of training data) (if you want to separate omission/commission errors

By armine
(157 views)

David Wright Solution Enablement Specialist

David Wright Solution Enablement Specialist

Classifying and Extracting Unstructured Documents in KTM 6. David Wright Solution Enablement Specialist. Classification. Chaos to Order! What is this document. Reduces sorting, examining. Different Kinds of Classification. Layout Classification

By cullen
(175 views)

Enhancing Text Classifiers to Identify Disease Aspect Information

Enhancing Text Classifiers to Identify Disease Aspect Information

Enhancing Text Classifiers to Identify Disease Aspect Information. Rey-Long Liu Dept. of Medical Informatics Tzu Chi University Taiwan. Outline. Research background Problem definition The proposed approach: IDAI Empirical evaluation Conclusion. Research Background.

By brice
(70 views)

Modeling Natural Text

Modeling Natural Text

Modeling Natural Text. David Kauchak CS458 Fall 2012. Admin. Final project Paper draft due next Friday by midnight Saturday, I’ll e-mail out 1-2 paper drafts for you to read Send me your reviews by Sunday at midnight Monday morning, I’ll forward these so you can integrate comments

By enan
(110 views)

Bayesian Learning Application to Text Classification Example: spam filtering

Bayesian Learning Application to Text Classification Example: spam filtering

KI2 - 3. Bayesian Learning Application to Text Classification Example: spam filtering. Marius Bulacu & prof. dr. Lambert Schomaker. Kunstmatige Intelligentie / RuG. Founders of Probability Theory. Pierre Fermat (1601-1665, France). Blaise Pascal (1623-1662, France).

By kamali
(118 views)

IR Homework #2

IR Homework #2

IR Homework #2. By J. H. Wang May 9, 2014. Programming Exercise #2: Text Classification. Goal: to classify each document into predefined categories Input : Reuters-21578 test collection predefined categories labeled documents for training test documents for testing

By bess
(120 views)

Text Classification

Text Classification

Text Classification. Chapter 2 of “Learning to Classify Text Using Support Vector Machines” by Thorsten Joachims, Kluwer, 2002. Text Classification (TC) : Definition.

By lali
(150 views)

Title Context-based Term Frequency Assessment for Text Classification (by Rey-Long Liu) Goal

Title Context-based Term Frequency Assessment for Text Classification (by Rey-Long Liu) Goal

Title Context-based Term Frequency Assessment for Text Classification (by Rey-Long Liu) Goal Improving various kinds of text classifiers by term context recognition (TCR) Method Employing TCR to refine term frequency (TF) assessment Applying the TF assessment to various classifiers Result

By kolya
(91 views)

EPL660: DATA CLASSIFICATION

EPL660: DATA CLASSIFICATION

EPL660: DATA CLASSIFICATION. Relevance feedback revisited. In relevance feedback, the user marks a few documents as relevant / nonrelevant The choices can be viewed as classes or categories For several documents, the user decides which of these two classes is correct

By archer
(112 views)

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