• 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 scikit-learn neural-networks conditional-random-fields NER word2vec syntactic-dependencies evaluation_metrics document-classification classification SyntaxNet NLTK word-embeddings tokenization tf-idf stanford-NER relationship-extraction named-entity-recognition naive-bayes multi-label-classification maximum-entropy-markov-models logistic-regression language-models information-extraction imbalanced_data hyperparameter-optimization hidden-markov-models grid-search glove gensim fasttext doc2vec dependency-graph deep-learning convolutional-neural-networks character-language-models character-embeddings PyData LSTM ELMo BERT