One approach to natural language processing that has gained tremendous traction recently is the vecotrization of words to represent their representation in a complex context. Deep Learning based sentiment analysis and general classifiers help improve the accuracy compared to results achieved with “classical” text analysis approaches. Tools like Word2Vec or GloVe are based on trained vectorized learning models that help in building commodity NLP available to a broad range of audience. Here is a list of resources to help you get started:
- Deep Learning for Natural Language Processing – Text By the Bay 2015 (Youtube)
- Deep Learning – Prof. Geoff Hinton (Youtube)
- Linguistic Regularities in Sparse and Explicit Word Representations (pdf)(slides)
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Efficient Estimation of Word Representations in Vector Space – Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
- word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method, arXiv 2014. – Goldberg, Y., and Levy, O.
- GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing
- A Word is Worth a Thousand Vectors. MultiThreaded, StitchFix, 11 March 2015. (Youtube)
- A Neural Network For Factoid Question Answering Over Paragraphs. Proceedings of EMNLP 2014 – Iyyer, M., Boyd-Graber, J., Claudino, L., Socher, R., and Daume III, H.
- Deep or Shallow, NLP is Breaking Out
- Deep Learning in a Nutshell (Part 1)(Part 2)(Part 3)
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