• Word representations: https://dl.acm.org/citation.cfm?id=1858721
• One-hot encoding: https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/
• Representational learning: https://github.com/anujgupta82/Representation-Learning-for-NLP
• N-grams: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.9367
• TF-IDF: https://nlp.stanford.edu/IR-book/html/htmledition/inverse-document-frequency-1.html
• Mikolov et al. 2013: https://arxiv.org/abs/1310.4546
• Maas and cgpotts paper: https://web.stanford.edu/~cgpotts/papers/wvSent_acl2011.pdf
• Bag of words in scikit-learn: http://scikit-learn.org/stable/modules/feature_extraction.html#the-bag-of-words-representation
• Kaggle word2vec https://www.kaggle.com/c/word2vec-nlp-tutorial
• Heap's law: https://en.wikipedia.org/wiki/Heaps%27_law
• Distributed representations of sentences and documents, Mikolov et al: https...