References

Chapter 3

• 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...

Chapter 4

• Vladimir Zolotov and David Kung 2017, Analysis and Optimization of fastText Linear Text Classifier, http://arxiv.org/abs/1702.05531
• Text classification of linear models, http://www.cs.umd.edu/class/fall2017/cmsc723/slides/slides_03.pdf
• What is text classification, Stanford, https://nlp.stanford.edu/IR-book/html/htmledition/the-text-classification-problem-1.html#sec:classificationproblem
• https://nlp.stanford.edu/IR-book/html/htmledition/text-classification-and-naive-bayes-1.html
• https://research.fb.com/fasttext/
• Bag of tricks for efficient classification, arXiv:1607.01759v3 [cs.CL] 9 Aug 2016
• https://github.com/poliglot/fasttext
• Joseph Turian, Lev Ratinov, and Yoshua Bengio, 2010, Word representations: A simple and general method for semi-supervised learning, In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), Association...

Chapter 5

• Software Framework for Topic Modelling with Large Corpora, Radim, 2010
• Gensim fastText Tutorial: https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/FastText_Tutorial.ipynb
• P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, Enriching Word Vectors with Subword Information, https://arxiv.org/abs/1607.04606
• http://proceedings.mlr.press/v37/kusnerb15.pdf
• Tomas Mikolov, Quoc V Le, Ilya Sutskever, 2013, (Exploiting Similarities among Languages for Machine Translation) (https://arxiv.org/pdf/1309.4168.pdf)
• Georgiana Dinu, Angelikie Lazaridou, and Marco Baroni. 2014, Improving zero-shot learning by mitigating the hubness problem (https://arxiv.org/pdf/1412.6568.pdf)
• The fastText normalization, https://www.kaggle.com/mschumacher/using-fasttext-models-for-robust-embeddings/notebook
• Luong, Minh-Thang and Socher, Richard and Manning, Christopher D. 2013, (Better...

Chapter 6

• Yoav Goldberg (2015), A Primer on Neural Network Models for Natural
  Language Processing, (https://arxiv.org/abs/1510.00726)
• http://www.wildml.com/2015/11/understanding-convolutional-neural-networksfor-nlp/
• http://mathworld.wolfram.com/Convolution.html
• http://www.joshuakim.io/understanding-how-convolutional-neural-network-cnn-perform-text-classification-with-word-embeddings/
• https://machinelearningmastery.com/best-practices-document-classification-deep-learning/
• https://keras.io/layers/embeddings/
• https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html
• https:/​/​pytorch.​org/​docs/​master/​nn.​html
• https://stackoverflow.com/a/35688187
• http://www.brightideasinanalytics.com/rnn-pretrained-word-vectors/

Chapter 7

• https://developer.android.com/training/basics/firstapp/
• https://github.com/sszuev/fastText_java
• https://stackoverflow.com/a/35369267
• https://developer.android.com/studio/publish/app-signing
• https://github.com/vinhkhuc/JFastText/blob/master/examples/api/src/main/java/ApiExample.java
• The fastText issues: https://github.com/vinhkhuc/JFastText/issues/28
• https://github.com/linkfluence/fastText4j/tree/master/src/main/java/fasttext