'Latent semantic indexing' presentation slideshows

An Introduction To Matrix Decomposition and Graphical Model

An Introduction To Matrix Decomposition and Graphical Model. Lei Zhang/Lead Researcher Microsoft Research Asia 2012-04-17. Outline. Matrix Decomposition PCA, SVD, NMF LDA, ICA, Sparse Coding, etc. Graphical Model Basic concepts in probabilistic machine learning EM pLSA LDA

1.58k views • 131 slides

Latent Dirichlet Allocation( LDA)

Latent Dirichlet Allocation( LDA). Week 8. Motivation. What do we want to do with text corpora? classification, novelty detection, summarization and similarity/relevance judgments. Given a text corpora or other collection of discrete data we wish to: Find a short description of the data.

826 views • 55 slides

15-853:Algorithms in the Real World

15-853:Algorithms in the Real World. Indexing and Searching I (how google and the likes work). Indexing and Searching Outline. Introduction: model, query types Inverted File Indices: Compression, Lexicon, Merging Vector Models: Latent Semantic Indexing:

588 views • 46 slides

Chapter 2 Modeling

Chapter 2 Modeling. Modern Information Retrieval by R. Baeza-Yates and B. Ribeiro-Neto. Introduction. Traditional information retrieval systems usually adopt index terms to index and retrieve documents.

653 views • 45 slides

DATA MINING LECTURE 8

DATA MINING LECTURE 8. Sequence Segmentation Dimensionality Reduction. SEQUENCE SEGMENTATION. Why deal with sequential data?. Because all data is sequential ? All data items arrive in the data store in some order Examples transaction data documents and words

468 views • 36 slides

Linear Algebra and Geometric Approaches to Meaning 3b. Distributed Semantics

400 views • 33 slides

Information Retrieval: Foundation to Web Search

413 views • 32 slides

INTEGRATING NETWORK STORAGE INTO INFORMATION RETRIEVAL APPLICATIONS

INTEGRATING NETWORK STORAGE INTO INFORMATION RETRIEVAL APPLICATIONS. Svetlana Y. Mironova The University of Tennessee, Knoxville Spring 2003. Topics of Discussion. Motivation General Text Parser (GTP) Network Storage Stack GTP with Network Storage Implementation Challenges Performance

422 views • 29 slides

Language and Learning

382 views • 27 slides

SVD & LSI

SVD & LSI. ML Reading Group Jan-24-2006 Presenter: Zheng Zhao. SVD ( Singular value decomposition). Vector Norm Matrix Norm Singular value decomposition The application of SVD. vector norm . A vector norm has the following properties. 1. || x || ? 0 (non-negative)

767 views • 27 slides

Latent Semantic Indexing for the Routing Problem

453 views • 26 slides

Information Retrieval WS 2012 / 2013

Information Retrieval WS 2012 / 2013. Lecture 8, Wednesday December 12 th , 2012 (Synonyms, Latent Semantic Indexing). Prof. Dr. Hannah Bast Chair of Algorithms and Data Structures Department of Computer Science University of Freiburg. Overview of this lecture. Organizational

364 views • 25 slides

Vector space models of word meaning

Vector space models of word meaning. Katrin Erk. Geometric interpretation of lists of feature/value pairs. In cognitive science: representation of a concept through a list of feature/value pairs Geometric interpretation: Consider each feature as a dimension

667 views • 21 slides

Latent Semantic Indexing

Latent Semantic Indexing. Jieping Ye Department of Computer Science & Engineering Arizona State University http://www.public.asu.edu/~jye02. Singular Value Decomposition. orthogonal. diagonal. orthogonal. Some Properties of SVD. Some Properties of SVD.

405 views • 20 slides

Latent Semantic Analysis

Latent Semantic Analysis. Keith Trnka. Latent Semantic Indexing. Application of Latent Semantic Analysis (LSA) to Information Retrieval Motivations Unreliable evidence synonomy: Many words refer to same object Affects recall polysemy: Many words have multiple meanings Affects precision.

470 views • 18 slides

Lecture 07: Data Transform II

Lecture 07: Data Transform II. September 28, 2010 COMP 150-12 Topics in Visual Analytics. Lecture Outline. Data Retrieval Methods for increasing retrieval speed: Pre-computation Pre-fetching and Caching Levels of Detail (LOD) Hardware support Data transform (pre-processing)

269 views • 17 slides

Effective Advertising on Video

Effective Advertising on Video. Hamed Sadeghi Neshat. Introduction. With Internet delivery of video content surging to an unprecedented level, video advertising is becoming increasingly pervasive The main problem in video advertising is intrusiveness and irrelevancy.

403 views • 13 slides

CSM06 Information Retrieval

198 views • 13 slides

Using TF-IDF to Determine Word Relevance in Document Queries

Using TF-IDF to Determine Word Relevance in Document Queries. Juan Ramos juramos@cs.rutgers.edu Department of Computer Science, Rutgers University, 23515 BPO Way, Piscataway, NJ, 08855. Information Retrieval Problem.

475 views • 10 slides

Final Review

Final Review. 7-Text Mining. Unstructured Data Two modes of mining: analysis vs. retrieval Precision vs. Recall as metric With lots of data you can find anything Tools for text mining Stopwords, stemming Term document matrix TF-IDF Latent Semantic Indexing (LSI)

176 views • 8 slides