PySeqLab: an open source Python package for sequence labeling and segmentation.MOTIVATION
 Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks.  Using PySeqLab, we built models (classifiers) and evaluated their performance in three different domains:  PySeqLab is available through https://bitbucket.org/A_2/pyseqlab with tutorials and documentation.



PySeqLab
Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks. 
Python Sequence Labeling
In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data.


 It implements CRFs models, that is discriminative models from  (i) first-order to higher-order linear-chain CRFs, and from  (ii) first-order to higher-order semi-Markov CRFs (semi-CRFs). 
CRFs
In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data.



conditional random fields
(i) stochastic gradient descent (SGD) and its multiple variations, 
SGD
(i) stochastic gradient descent (SGD) and its multiple variations, 
stochastic gradient descent
(ii) structured perceptron with multiple averaging schemes supporting exact and inexact search using 'violation-fixing' framework, 
structured perceptron
(iv) an interface for Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited-memory BFGS algorithms. 
BFGS
(iv) an interface for Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited-memory BFGS algorithms. 
limited-memory BFGS
Viterbi and Viterbi A* are used for inference and decoding of sequences. 
Viterbi
Viterbi and Viterbi A* are used for inference and decoding of sequences. 
Viterbi A*