'Document classification' presentation slideshows

Text Mining for Biomedicine: Techniques & tools

Text Mining for Biomedicine: Techniques & tools. Sophia Ananiadou, Chikashi Nobata,Yutaka Sasaki, Yoshimasa Tsuruoka School of Computer Science National Centre for Text Mining www.nactem.ac.uk Sophia.Ananiadou@manchester.ac.uk. Outline. Challenges / objectives of TM in biomedicine

1.71k views • 146 slides

Statistical NLP Winter 2009

730 views • 58 slides

Information Retrieval and Web Search

716 views • 56 slides

Text Classification with Limited Labeled Data

Text Classification with Limited Labeled Data. Andrew McCallum mccallum@cs.cmu.edu Just Research (formerly JPRC) Center for Automated Learning and Discovery, Carnegie Mellon University.

645 views • 56 slides

Text Classification with Limited Labeled Data

Text Classification with Limited Labeled Data. Andrew McCallum mccallum@cs.cmu.edu Just Research (formerly JPRC) Center for Automated Learning and Discovery, Carnegie Mellon University.

567 views • 56 slides

by

Classification and clustering methods development and implementation for unstructured documents collections. by Osipova Nataly St.Petesburg State University Faculty of Applied Mathematics and Control Processes Department of Programming Technology. Contents. Introduction

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NJIT CIS 392 Text Processing, Retrieval, and Mining Spring 2003

NJIT CIS 392 Text Processing, Retrieval, and Mining Spring 2003. Materials: Sullivan Ch 8 An Introduction to Neural Networks, Ch 1, by Kevin Gurney (optional) http://www.shef.ac.uk/psychology/gurney/notes/. Document Space. Visualizing objects of high dimensional datasets is difficult.

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Hierarchical Classification of Documents with Error Control

391 views • 32 slides

Introduction to Information Retrieval

Introduction to Information Retrieval. Slides by me,. The Inverted Index. Indexing. Indexing is a technique borrowed from databases An index is a data structure that supports efficient lookups in a large data set E.g., hash indexes, R-trees, B-trees, etc. Document Retrieval.

448 views • 31 slides

Understanding the Language of Virus Proteins to Automatically Detect Drug Resistance

Understanding the Language of Virus Proteins to Automatically Detect Drug Resistance. Betty Cheng, Jaime Carbonell Language Technologies Institute, School of Computer Science Carnegie Mellon University. Outline. HIV & Drug Resistance Phenotype Prediction Models Machine Learning

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Example

Example. 16,000 documents 100 topic Picked those with large p(w|z). New document?. Given a new document, compute and words allocated to each topic approximates p ( z n | w ) See cases where these values are relatively large 4 topics found. Unseen document (contd.).

420 views • 28 slides

Hierarchical Topic Models and the Nested Chinese Restaurant Process

Hierarchical Topic Models and the Nested Chinese Restaurant Process. Blei, Griffiths, Jordan, Tenenbaum presented by Rodrigo de Salvo Braz. Document classification. One-class approach: one topic per document, with words generated according to the topic. For example, a Naive Bayes model.

401 views • 26 slides

Word2Vec Explained

Word2Vec Explained. Jun Xu Harbin Institute of Technology China. Word Similarity & Relatedness. How similar is pizza to pasta ? How related is pizza to Italy ? Representing words as vectors allows easy computation of similarity Measure the semantic similarity between words

474 views • 21 slides

Ming- wei Chang University of Illinois at Urbana-Champaign Wen -tau Yih and Christopher Meek

297 views • 20 slides

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???? & ????. ??? 2013.12.15. T. A. Runkler, J.C. Bezdek, Web mining with relational clustering, International Journal of Approximate Reasoning , vol . 32 , pp. 217-236, 2003. Introduction Objective function clustering Alternating cluster estimation (ACE) Clustering relational data

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multiclass

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Naive Bayes for Document Classification

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Link Distribution in Wikipedia

Link Distribution in Wikipedia. [0324] KwangHee Park. Table of contents. Introduction Cluster using LDA Experiment Disease, settlement Demo Considering Application. Introduction. Why focused on Link

233 views • 10 slides