document-classification
- Convolutional Neural Networks for Text Classification (31 Mar 2018)
Convolutional Neural Networks for Sentence Classification - 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 reference-post gensim fasttext evaluation_metrics document-classification classification SyntaxNet NLTK LSTM wikidata tokenization tf-idf stanford-NER sparql seq2seq relationship-extraction recurrent-neural-networks portuguese pandas 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 cheat-sheet character-language-models character-embeddings attention RNN PyData KOVENS GRU ELMo BERT