Corpus ID: 11231900

Log-linear models and conditional random fields

@inproceedings{Elkan2007LoglinearMA,
  title={Log-linear models and conditional random fields},
  author={C. Elkan},
  year={2007}
}
This document describes log-linear models, which are a far-reaching extension of logistic regression, and conditional random fields (CRFs), which are a special case of log-linear models. Section 1 explains what a log-linear model is, and introduces feature functions. Section 2 then presents linear-chain CRFs as an example of log-linear models, and Section 3 explains the special algorithms that make inference tractable for these CRFs. Section 4 gives a general derivation of the gradient of a log… Expand
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References

SHOWING 1-10 OF 16 REFERENCES
Efficiently Inducing Features of Conditional Random Fields
TLDR
This paper presents an efficient feature induction method for CRFs founded on the principle of iteratively constructing feature conjunctions that would significantly increase conditional log-likelihood if added to the model. Expand
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TLDR
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data. Expand
Training conditional random fields via gradient tree boosting
TLDR
This paper describes a new method for training CRFs by applying Friedman's (1999) gradient tree boosting method, which scales linearly in the order of the Markov model and in the Order of the feature interactions, rather than exponentially like previous algorithms based on iterative scaling and gradient descent. Expand
An Introduction to Conditional Random Fields for Relational Learning
TLDR
A solution to this problem is to directly model the conditional distribution p(y|x), which is sufficient for classification, and this is the approach taken by conditional random fields. Expand
Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms
TLDR
Experimental results on part-of-speech tagging and base noun phrase chunking are given, in both cases showing improvements over results for a maximum-entropy tagger. Expand
Discriminative random fields: a discriminative framework for contextual interaction in classification
  • Sanjiv Kumar, M. Hebert
  • Mathematics, Computer Science
  • Proceedings Ninth IEEE International Conference on Computer Vision
  • 2003
TLDR
This work presents discriminative random fields (DRFs), a discrim inative framework for the classification of image regions by incorporating neighborhood interactions in the labels as well as the observed data that offers several advantages over the conventional Markov random field framework. Expand
Large Margin Methods for Structured and Interdependent Output Variables
TLDR
This paper proposes to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation and presents a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems. Expand
Training Conditional Random Fields for Maximum Labelwise Accuracy
TLDR
This work gives a gradient-based procedure for minimizing an arbitrarily accurate approximation of the empirical risk under a Hamming loss function. Expand
Max-Margin Markov Networks
TLDR
Maximum margin Markov (M3) networks incorporate both kernels, which efficiently deal with high-dimensional features, and the ability to capture correlations in structured data, and a new theoretical bound for generalization in structured domains is provided. Expand
Conditional Random Fields: An Introduction
The task of assigning label sequences to a set of observation sequences arises in many fields, including bioinformatics, computational linguistics and speech recognition [6, 9, 12]. For example,Expand
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