gensim


  • Portuguese Word Embeddings (03 Nov 2019)

  • Document Classification (01 Apr 2017)
    An introduction to the Document Classification task, in this case in a multi-class and multi-label scenario, proposed solutions include TF-IDF weighted vectors, an average of word2vec words-embeddings and a single vector representation of the document using doc2vec. Includes code using Pipeline and GridSearchCV classes from scikit-learn.

viterbi sequence-prediction pos-tags neural-networks word2vec scikit-learn conditional-random-fields NER word-embeddings syntactic-dependencies gensim fasttext evaluation_metrics document-classification classification SyntaxNet NLTK LSTM tokenization tf-idf stanford-NER seq2seq relationship-extraction recurrent-neural-networks portuguese nlp named-entity-recognition naive-bayes multi-label-classification maximum-entropy-markov-models machine-translation logistic-regression language-models information-extraction imbalanced_data hyperparameter-optimization hidden-markov-models grid-search glove embeddings doc2vec dependency-graph deep-learning data-challenge convolutional-neural-networks conference character-language-models character-embeddings attention RNN PyData KOVENS GRU ELMo BERT