# Log-linear models and conditional random fields

@inproceedings{Elkan2007LoglinearMA, title={Log-linear models and conditional random fields}, author={Charles Peter 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…

## 32 Citations

### Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge

- Computer ScienceArXiv
- 2016

It is argued that the log-linear and the neural-network components contribute complementary strengths to the LL-RNN: the LL aspect allows the model to incorporate rich prior knowledge, while the NN aspect, according to the "representation learning" paradigm, allows themodel to discover novel combination of characteristics.

### Conditional Random Fields for Word Hyphenation

- Computer Science
- 2013

The experiment results show that Collins Perceptron is the most efficient method for training linear-chain CRFs and ABE is a better feature representation scheme that outperforms RBE by 7.9% accuracy and the design is reasonable by comparing it to the state-of-the-art [2].

### Analyzing Sequence Data Based on Conditional Random Fields with Co-training

- Computer Science2012 Eighth International Conference on Computational Intelligence and Security
- 2012

A novel model is proposed named Conditional Random Fields with Co-training (Co-CRF), which can produce a more accurate analysis than the traditional CRF, especially with very limited training data.

### A Conditional Random Field Model for Context Aware Cloud Detection in Sky Images

- Computer Science, Environmental ScienceArXiv
- 2019

It is shown that very high cloud detection accuracy can be achieved by combining a discriminative classifier and a higher order clique potential in a CRF framework.

### On accelerating iterative algorithms with CUDA: A case study on Conditional Random Fields training algorithm for biological sequence alignment

- Computer Science2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)
- 2010

The parallelized algorithm for the Gradient Ascent based CRF training methods for biological sequence alignment is designed, which flexibly parallelized the different iterative computation patterns, and the according optimization methods are presented.

### Features we trust!

- Computer Science2015 IEEE International Conference on Image Processing (ICIP)
- 2015

This work investigates the problem of image classification within a supervised learning framework that exploits implicit mutual information in different visual features and their associated classifiers using a fully connected conditional random fields (CRF) over clusters.

### Chinese Lexical Analysis with Deep Bi-GRU-CRF Network

- Computer ScienceArXiv
- 2018

A deep Bi-GRU-CRF network that jointly models word segmentation, part-of-speech tagging and named entity recognition tasks and achieves a 95.5% accuracy on the test set, roughly 13% relative error reduction over the best Chinese lexical analysis tool.

### Natural language processing: an introduction

- Computer ScienceJ. Am. Medical Informatics Assoc.
- 2011

The historical evolution of NLP is described, and common NLP sub-problems in this extensive field are summarized, and possible future directions for NLP are considered.

### A social recommendation framework based on multi-scale continuous conditional random fields

- Computer ScienceCIKM
- 2009

A novel model, Multi-scale Continuous Conditional Random Fields (MCCRF), is proposed as a framework to solve above problems for social recommendations, where relational dependency within predictions is modeled by the Markov property, thus predictions are generated simultaneously and can help each other.

### Hyphenation with Conditional Random Field

- Computer Science
- 2012

In this project, we approach the problem of English-word hyphenation using a linear-chain conditional random field model. We measure the effectiveness of different feature combinations and two…

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