**sequence-prediction**

- Named-Entity Recognition based on Neural Networks (22 Oct 2018)

This blog post reviews some of the recently 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 its 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 out 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 its relationships with the Naive Bayes approach.

viterbi

sequence-prediction

scikit-learn

pos-tags

conditional-random-fields

NER

word2vec

word-embeddings

triplet-loss

syntactic-dependencies

sentence-transformers

relationship-extraction

neural-networks

fine-tuning

evaluation_metrics

embeddings

coursera

conference

SyntaxNet

NLTK

LSTM

CRF

wikidata

transformers

tokenization

tf-idf

text-summarisation

semantic-web

resources

reference-post

production

portuguese

political-science

named-entity-recognition

naive-bayes

multi-label-classification

monitoring

mlops

maximum-entropy-markov-models

logistic-regression

llms

language-models

information-extraction

imbalanced_data

hyperparameter-optimization

hidden-markov-models

grid-search

gensim

generative-ai

fasttext

document-classification

doc2vec

deployment

dependency-graph

dataset

data-challenge

convolutional-neural-networks

contrastive-learning

classification

books

attention

SPARQL

RNN

PyData

KOVENS

GRU