• Named-Entity Recognition based on Neural Networks (22 Oct 2018)
This blog post review some of the recent proposed methods to perform named-entity recognition using neural networks.

• Conditional Random Fields for Sequence Prediction (13 Nov 2017)
An introduction to Linear-Chain Conditional Random Fields, explaining what was the motivation behind it's proposal and making a comparison with two other sequence models, Hidden-Markov Model, and Maximum Entropy Markov Model.

• Maximum Entropy Markov Models and Logistic Regression (12 Nov 2017)
This blog post is an introduction to Maximum Entropy Markov Model, it points the fundamental difference between discriminative and generative models, and what are the main advantages of the Maximum Entropy Markov Model over the Naive Bayes model.

• Hidden Markov Model and Naive Bayes relationship (11 Nov 2017)
An introduction to Hidden Markov Models, one of the first proposed algorithms for sequence prediction, and it's relationships with the Naive Bayes approach.

viterbi sequence-prediction pos-tags scikit-learn conditional-random-fields NER syntactic-dependencies evaluation_metrics document-classification classification SyntaxNet NLTK word2vec tokenization tf-idf stanford-NER relationship-extraction neural-networks named-entity-recognition naive-bayes multi-label-classification maximum-entropy-markov-models logistic-regression information-extraction imbalanced_data hyperparameter-optimization hidden-markov-models grid-search gensim doc2vec dependency-graph deep-learning convolutional-neural-networks PyData LSTM