This page contains material on, or relating to, conditional random fields.
I shall continue to update this page as research on conditional random fields advances, so do check back periodically.
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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. Department of Computer and Information Science, University of Pennsylvania, 2004. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
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. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001), 2001.
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. We present conditional random fields, a framework for building probabilistic models to segment and label sequence data.
CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models.
Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states.
We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
This thesis explores a number of parameter estimation techniques for conditional random fields, a recently introduced probabilistic model for labelling and segmenting sequential data. Statistical learning problems in many fields involve sequential data.
Theoretical and practical disadvantages of the training techniques reported in current literature on CRFs are discussed. This paper formalizes the principal learning tasks and describes the methods that have been developed within the machine learning research community for addressing these problems.
We hypothesise that general numerical optimisation techniques result in improved performance over iterative scaling algorithms for training CRFs. In Structural, Syntactic, and Statistical Pattern Recognition; Lecture Notes in Computer Science, Vol. These methods include sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks. In Proceedings of the 2003 Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT/NAACL-03), 2003.
Experiments run on a subset of a well-known text chunking data set confirm that this is indeed the case. The paper also discusses some open research issues. Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifers applied at each sequence position.
This is a highly promising result, indicating that such parameter estimation techniques make CRFs a practical and efficient choice for labelling sequential data, as well as a theoretically sound and principled probabilistic framework. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods.