• Evaluation Metrics, ROC-Curves and imbalanced datasets (19 Aug 2018)
    This blog post describes some evaluation metrics used in NLP, it points out where we should use each one of them and the advantages and disadvantages of each.

  • 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.

pos-tags viterbi sequence-prediction scikit-learn syntactic-dependencies evaluation_metrics document-classification conditional-random-fields classification SyntaxNet NLTK NER word2vec tokenization tf-idf stanford-NER relationship-extraction 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