'Maximum entropy model' presentation slideshows

Semantic Inference for Question Answering

Semantic Inference for Question Answering. Sanda Harabagiu Department of Computer Science University of Texas at Dallas. Srini Narayanan International Computer Science Institute Berkeley, CA. and. Outline. Part I. Introduction: The need for Semantic Inference in QA

2.07k views • 168 slides

Semantic Inference for Question Answering

Semantic Inference for Question Answering. Sanda Harabagiu Department of Computer Science University of Texas at Dallas. Srini Narayanan International Computer Science Institute Berkeley, CA. and. Outline. Part I. Introduction: The need for Semantic Inference in QA

1.69k views • 168 slides

Cross-Document Coreference: Methodologies, Evaluations and Applications

Cross-Document Coreference: Methodologies, Evaluations and Applications. Amit Bagga Ask Jeeves, Inc. Outline. Motivation and Definition Comparison with Within-Document Coreference, WSD and other NL tasks Methodologies for Entity Cross-Document Coreference

889 views • 65 slides

Maximum Entropy Model

Maximum Entropy Model. LING 572 Fei Xia 02/08/07. Topics in LING 572. Easy: kNN, Rocchio, DT, DL Feature selection, binarization, system combination Bagging Self-training. Topics in LING 572. Slightly more complicated Boosting Co-training Hard (to some people): MaxEnt EM. History.

1.1k views • 63 slides

Problem

Text Mining, Information and Fact Extraction Part 2: Machine Learning Techniques Marie-Francine Moens Department of Computer Science Katholieke Universiteit Leuven, Belgium sien.moens@cs.kuleuven.be. Problem.

749 views • 62 slides

Maximum Entropy Model

898 views • 60 slides

Problem definition

Text Mining, Information and Fact Extraction Part 3: Machine Learning Techniques (continued) Marie-Francine Moens Department of Computer Science Katholieke Universiteit Leuven, Belgium sien.moens@cs.kuleuven.be. Problem definition.

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Maximum Entropy Model

787 views • 57 slides

Classical Probabilistic Models and Conditional Random Fields

Classical Probabilistic Models and Conditional Random Fields. Roman Klinger 1 , Katrin Tomanek 2 Algorithm Engineering Report (TR07-2-013), 2007 1. Dortmund University of Technology Department of Computer Science 2. Language & Information Engineering (JULIE) Lab. Abstract. Introduction

801 views • 52 slides

Probabilistic Models for Images Markov Random Fields

Probabilistic Models for Images Markov Random Fields Applications in I mage S egmentation and Texture Modeling Ying Nian Wu UCLA Department of Statistics IPAM July 22, 2013. Outline Basic concepts, properties, examples Markov chain Monte Carlo sampling Modeling textures and objects

605 views • 42 slides

Maximum Entropy (ME) Maximum Entropy Markov Model (MEMM) Conditional Random Field (CRF )

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Maximum Entropy Model

491 views • 28 slides

Middle Term Exam

433 views • 27 slides

Unsupervised Transfer Classification

Unsupervised Transfer Classification. Application to Text Categorization Tianbao Yang , Rong Jin, Anil Jain, Yang Zhou, Wei Tong Michigan State University. Overview. Introduction Related Work Unsupervised Transfer Classification Problem Definition Approach & Analysis Experiments

261 views • 24 slides

COMP 4332 Tutorial 2 Feb 18 Chen Zhao

Project 1: KDD 2009 Orange Challenge. COMP 4332 Tutorial 2 Feb 18 Chen Zhao. All information on this website. http://www.kddcup-orange.com/. Record KDD Cup Participation. The story behind the challenge. French Telecom company Orange . Task: predict the propensity of customers to

272 views • 16 slides

Graph-based Event Coreference Resolution

Graph-based Event Coreference Resolution. Zheng Chen and Heng Ji City University of New York August 7, 2009. Outline. Event Coreference Resolution Task Graph-based event coreference resolution Spectral Graph Clustering Coreference Matrix Construction Global Weights Maximum Entropy based

458 views • 12 slides