'Term document matrix' presentation slideshows

Matrix Decomposition Methods in Information Retrieval

Matrix Decomposition Methods in Information Retrieval. Thomas Hofmann Department of Computer Science Brown University www.cs.brown.edu/people/th (& Chief Scientist, RecomMind Inc.). In collaboration with: Jan Puzicha, UC Berkeley & RecomMind David Cohen, CMU & Burning Glass. Overview .

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INFSCI 2140 Information Storage and Retrieval Lecture 5: Text Analysis

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Outline

Outline. Support Vector Machines Issues in the classification of text documents. Support Vector Machines. 2-class training data decision boundary ? linear separator criterion : being maximally far away from any data point ? determines classifier margin

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Information Retrieval

Information Retrieval. hussein suleman uct cs 3003s 2006. Introduction. Information retrieval is the process of locating the most relevant information to satisfy a specific information need. Traditionally, we used databases and keywords to locate information.

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Information Management Information Retrieval

Information Management Information Retrieval. hussein suleman uct cs 303 2004. Introduction. Information retrieval is the process of locating the most relevant information to satisfy a specific information need. Traditionally, we used databases and keywords to locate information.

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Daniel M. Dunlavy , Timothy M. Shead, Patricia J. Crossno Sandia National Laboratories

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A Chinese Information Retrieval System Using SDD

A Chinese Information Retrieval System Using SDD. Introduction SDD algorithm Compute SDD Term Weighting Computing the Similarity Term Extracting System Implementation Experiments and Results Web applications Conclusion. Introduction.

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Indexing by Latent Semantic Analysis

Indexing by Latent Semantic Analysis. Written by Deerwester, Dumais, Furnas, Landauer, and Harshman (1990) Reviewed by Cinthia Levy. Latent Semantic Indexing. Term-matching Most retrieval systems match words of a query (keywords) with words of a document. Problem

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Outline

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Bayesian Networks in Document Clustering

Bayesian Networks in Document Clustering. Slawomir Wierzchon , Mieczyslaw Klopotek Michal Draminski Krzysztof Ciesielski Mariusz Kujawiak Institute of Computer Science, Polish Academy of Sciences Warsaw.

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Bayesian Networks in Document Clustering

Bayesian Networks in Document Clustering. Slawomir Wierzchon , Mieczyslaw Klopotek Michal Draminski Krzysztof Ciesielski Mariusz Kujawiak Institute of Computer Science, Polish Academy of Sciences Warsaw.

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A comparative study of TF*IDF , LSI and multi-words for text classi?cation

A comparative study of TF*IDF , LSI and multi-words for text classi?cation. Presenter : Jian-Ren Chen Authors : W en Zhang , T aketoshi Y oshida , X ijin T ang 2011.ESWA. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Text Mining

Text Mining. with R and the tm package. Agenda. Motivation Preliminaries Operations Demo Thoughts System prerequisites Resources References. Motivation. Exciting new possibilities to deal with unstructured text data (tweets, news articles/feeds, customer complaints)

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Text Mining

Text Mining. with R and the tm package. Agenda. Motivation Preliminaries Operations Demo Thoughts System prerequisites Resources References. Motivation. Exciting new possibilities to deal with unstructured text data (tweets, news articles/feeds, customer complaints)

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Modern information retreival

Modern information retreival. Chapter. 02: Modeling (Latent Semantic Indexing). Latent Semantic Indexing. Classic IR might lead to poor retrieval due to: unrelated documents might be included in the answer set relevant documents that do not contain at least one index term are not retrieved

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Modern information retreival

Modern information retreival. Chapter. 02: Modeling (Latent Semantic Indexing). Latent Semantic Indexing. Classic IR might lead to poor retrieval due to: unrelated documents might be included in the answer set relevant documents that do not contain at least one index term are not retrieved

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Rendering

2-Dim Reduction. LSI. Rendering. scatter plot. source files. term-document matrix. thematic map. i nitial c onfiguration. TDM. Isomap. g lobal layout. f inal c onfiguration. proximities. MDS. local layout.

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