条件随机场学习资料汇总

introduction

• Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices.

• The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences.

• The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference.

• Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models.

• CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.

相关论文：

1.http://www.inference.org.uk/hmw26/papers/crf_intro.pdf
Hanna M. Wallach. Conditional Random Fields: An Introduction. Technical Report MS-CIS-04-21. Department of Computer and Information Science, University of Pennsylvania, 2004.
2.强推：
https://arxiv.org/PS_cache/arxiv/pdf/1011/1011.4088v1.pdf

博客参考资料

http://www.52nlp.cn/条件随机场文献阅读指南