'Topic models' presentation slideshows

Lecture 16: Unsupervised Learning from Text

Lecture 16: Unsupervised Learning from Text. Padhraic Smyth Department of Computer Science University of California, Irvine. Outline. General aspects of text mining Named-entity extraction, question-answering systems, etc Unsupervised learning from text documents Motivation

1.49k views • 132 slides

Topic Models for Social Network Analysis and Bibliometrics

Topic Models for Social Network Analysis and Bibliometrics. Andrew McCallum Computer Science Department University of Massachusetts Amherst. Joint work with ? Xuerui Wang, Natasha Mohanty, Andres Corrada, Chris Pal, Wei Li, David Mimno and Gideon Mann. Goal:.

1.28k views • 111 slides

Semantic Representations with Probabilistic Topic Models

Semantic Representations with Probabilistic Topic Models. Mark Steyvers Department of Cognitive Sciences University of California, Irvine. Joint work with: Tom Griffiths, UC Berkeley Padhraic Smyth, UC Irvine. Topic Models in Machine Learning.

1.2k views • 105 slides

Fast and Accurate Inference for Topic Models

Fast and Accurate Inference for Topic Models. James Foulds University of California, Santa Cruz Presented at eBay Research Labs. Motivation. There is an ever-increasing wealth of digital information available Wikipedia News articles Scientific articles Literature Debates

1.13k views • 95 slides

Padhraic Smyth Department of Information and Computer Science University of California, Irvine

906 views • 76 slides

Topic Models for Morphologically Rich Languages

Topic Models for Morphologically Rich Languages. Michael Elhadad, Meni Adler, Yoav Goldberg, Rafi Cohen 23 Jan 2011, Haifa Machine Translation and Morphologically-rich Languages. Topic Models. Unsupervised discovery of topics in text collection Useful to browse/explore large corpora by theme

848 views • 64 slides

Probabilistic Topic Models

Probabilistic Topic Models. Hongning Wang CS@UVa. Outline. General idea of topic models Basic topic models Probabilistic Latent Semantic Analysis ( pLSA ) Latent Dirichlet Allocation (LDA) Variants of topic models Summary. What is a Topic?.

944 views • 64 slides

Structured Topic Models: Jointly Modeling Words and Their Accompanying Modalities

Structured Topic Models: Jointly Modeling Words and Their Accompanying Modalities. Xuerui Wang Computer Science Department University of Massachusetts Amherst. Joint work with Andrew McCallum, Andres Corrada-Emmanuel, Chris Pal, Xing Wei and Natasha Mohanty. Probabilistic topic models.

703 views • 57 slides

Analyzing unstructured text with topic models

Analyzing unstructured text with topic models. Mark Steyvers Dep. of Cognitive Sciences & Dep. of Computer Science University of California, Irvine. collaborators: Padhraic Smyth, UC Irvine; Tom Griffiths UC Berkeley. Analyzing Unstructured Text. Pennsylvania Gazette (1728-1800)

657 views • 52 slides

Elementary Text Analysis & Topic Modeling

Elementary Text Analysis & Topic Modeling. Kristina Lerman University of Southern California. Why topic modeling. Volume of collections of text document is growing exponentially, necessitating methods for automatically organizing, understanding, searching and summarizing them

619 views • 47 slides

LDA Training System

LDA Training System. xueminzhao@tencent.com 8/22/2012. Outline. Introduction SparseLDA Rethinking LDA: Why Priors Matter LDA Training System Design: MapReduce -LDA. Outline. Introduction SparseLDA Rethinking LDA: Why Priors Matter LDA Training System Design: MapReduce -LDA.

673 views • 44 slides

SCIENCE MAPS

548 views • 43 slides

Joint Enhancement of Topic Modeling and Information Network Mining

Joint Enhancement of Topic Modeling and Information Network Mining. Mid-Year PI Report focusing on I3.2 Heng Ji City University of New York NSCTA/INARC. INARC Project Major Contributions. I3.2-Subtask 1:

485 views • 35 slides

Focused Crawling A New Approach to Topic-Specific Web Resource Discovery

Focused Crawling A New Approach to Topic-Specific Web Resource Discovery. Soumen Chakrabarti Martin van Den Berg Byron Dom. Portals and portholes. Popular search portals and directories Useful for generic needs Difficult to do serious research

466 views • 34 slides

Object-Graphs for Context-Aware Category Discovery

Object-Graphs for Context-Aware Category Discovery. Yong Jae Lee and Kristen Grauman University of Texas at Austin. Motivation. 1) reveal structure in very large image collections 2) greatly reduce annotation time and effort 3) training data is not always available. Unlabeled Image Data.

395 views • 31 slides

A Two-Dimensional Topic-Aspect Model for Discovering Multi-Faceted Topics

419 views • 23 slides

Automatic Labeling of Multinomial Topic Models

Automatic Labeling of Multinomial Topic Models. Qiaozhu Mei, Xuehua Shen, ChengXiang Zhai University of Illinois at Urbana-Champaign. Outline. Background: statistical topic models Labeling a topic model Criteria and challenge Our approach: a probabilistic framework Experiments Summary.

338 views • 16 slides

Conditional Topic Random Fields

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Topic Models in Text Processing

Topic Models in Text Processing. IR Group Meeting Presented by Qiaozhu Mei. Topic Models in Text Processing. Introduction Basic Topic Models Probabilistic Latent Semantic Indexing Latent Dirichlet Allocation Correlated Topic Models Variations and Applications. Overview. Motivation:

378 views • 15 slides

Query: NYT Article. P( topic ). the. four. remaining. directors. of. the. enron. corporation. who. oversaw. the. company. s. rise. to. the. top. 10. of. the. fortune. 500. and. its. collapse. into. bankruptcy. last. year quit today. the. resignations. of. the.

196 views • 1 slides