'Maximum entropy models' diaporamas de présentation

Maximum Entropy Model (II)

Maximum Entropy Model (II). LING 572 Fei Xia Week 6: 02/12-02/14/08. Outline. Overview The Maximum Entropy Principle Modeling** Decoding Training* Smoothing* Case study: POS tagging. Training. Algorithms. Generalized Iterative Scaling (GIS): ( Darroch and Ratcliff, 1972)

889 views • 69 slides

Conditional Random Fields For Speech and Language Processing

Conditional Random Fields For Speech and Language Processing. Jeremy Morris 10/27/2008. Outline. Background Maximum Entropy models and CRFs CRF Example SLaTe experiments with CRFs. Background. Conditional Random Fields (CRFs) Discriminative probabilistic sequence model

718 views • 54 slides

Building Maximum Entropy Text Classifier Using Semi-supervised Learning

Building Maximum Entropy Text Classifier Using Semi-supervised Learning. Zhang, Xinhua. For PhD Qualifying Exam Term Paper. Road map. Introduction: background and application Semi-supervised learning, especially for text classification (survey) Maximum Entropy Models (survey)

540 views • 53 slides

Question Answering as Question-Biased Term Extraction: A New Approach toward Multilingual QA

Question Answering as Question-Biased Term Extraction: A New Approach toward Multilingual QA. Yutaka Sasaki ATR NLP LAB ACL 2005. ??. ?????? Question-Biased Term Extraction (QBTE) ??????? Question Answering (QA) . QA v.s. QBTE

656 views • 52 slides

Conditional Random Fields An Overview

Conditional Random Fields An Overview. Jeremy Morris 01/11/2008. Outline. Background Maximum Entropy models and CRFs CRF Example SLaTe experiments with CRFs. Background. Conditional Random Fields (CRFs) Discriminative probabilistic sequence model

910 views • 50 slides

Part-of-speech tagging and chunking with log-linear models

Part-of-speech tagging and chunking with log-linear models. University of Manchester Yoshimasa Tsuruoka. Outline. POS tagging and Chunking for English Conditional Markov Models (CMMs) Dependency Networks Bidirectional CMMs Maximum entropy learning Conditional Random Fields (CRFs)

629 views • 42 slides

Generative and Discriminative Models in NLP: A Survey

834 views • 39 slides

Segmentation via Maximum Entropy Model

Segmentation via Maximum Entropy Model. Goals. Is it possible to learn the segmentation problem automatically? Using a model which is frequently used in NLP tasks - the Maximum Entropy model Finding features compatible to the segmentation task. Segmentation as a tagging problem.

409 views • 19 slides