NPTEL Applied Natural Language Processing

  1. Introduction, terminologies, empirical rules
  2. Word to Vectors
  3. Probability and Language Model
  4. Neural Networks for NLP
  5. Distributed word vectors (word embeddings)
  6. Recurrent Neural Network, Language Model
  7. Statistical Machine Translation
  8. Statistical Machine Translation, Neural Machine Translation
  9. Neural Machine Translation
  10. Conversation Modeling, Chat-bots, dialog agents, Question Processing
  11. Information Retrieval tasks using Neural Networks- Learn to Rank, Understanding Phrases, analogies
  12. Spelling Correction using traditional and Neural networks, end notes
  1. History, Features, and Typology of Language Corpora, Niladri and Arulmozi, Springer 2018
    Chapter 2: Features of a Corpus: Say No to Piracy!
  2. Deep Learning, Goodfellow et al, MIT Press, 2017
    The online version of the book available for free:
  3. Handbook of natural language processing, Nitin Indurkhya and Fred J Damerau, 2010. Stop Piracy!
  4. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Daniel Jurafsky and James H. Martin, 2000.
  5. Foundations of Statistical Natural Language Processing, Christopher D. Manning et al, 1999.
  6. An Introduction to Information Retrieval, Christopher D. Manning et al, 2009.
    Chapter 6: Scoring, term weighting and the vector space model.
  7. Python 3 text processing with NLTK 3 cookbook, Jacob Perkins, Packt, 2014.
  8. Linguistic Structure Prediction. Synthesis Lectures on Human Language Technologies, Noah A. Smith, 2011. Piracy. It’s a crime.


  1. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. 2014
  2. Yoshua Bengio et al. A Neural Probabilistic Language Model. 2003
  3. Peter F. Brown et al. Class-based N-gram Models of Natural Language1992
  4. Peter F. Brown et al. The Mathematics of Statistical Machine Translation: Parameter Estimation. 1993
  5. KyungHyun Cho et al. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. 2014
  6. Scott Deerwester et al. Indexing by latent semantic analysis. 1990
  7. Chris Dyer. Notes on Noise Contrastive Estimation and Negative Sampling. 2014
  8. Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing. 2015
  9. Nils Hadziselimovic et al. Forgetting Is Regulated via Musashi-Mediated Transnational Control of the Arp2/3 Complex.. 2014
  10. Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory. 1997
  11. Chiori Hori and Takaaki Hori. End-to-end Conversation Modeling Track in DSTC6. 2017
  12. Andrej Karpathy, Justin Johnson, and Fei-Fei Li. Visualizing and Understanding Recurrent Networks. 2015
  13. Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. Effective Approaches to Attention-based Neural Machine Translation. 2015
  14. Tomas Mikolov et al. Efficient Estimation of Word Representations in Vector Space. 2013
  15. Franz Josef Och and Hermann Ney. The Alignment Template Approach to Statistical Machine Translation. 2004
  16. F. Pedregosa et al. Scikit-learn: Machine Learning in Python. 2011
  17. Xin Rong. word2vec Parameter Learning Explained. 2014
  18. Fraser W. Smith and Lars Muckli. Nonstimulated early visual areas carry information about surrounding context. 2010


  1. Kyunghyun Cho et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. 2014
  2. Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. An Empirical Exploration of Recurrent Network Architectures. 2015
  3. Quoc Le and Tomas Mikolov. Distributed representations of sentences and documents. 2014
  4. Edward Loper and Steven Bird. NLTK: The Natural Language Toolkit. 2002
  5. Tomas Mikolov et al. Distributed Representations of Words and Phrase and Their Compositionality. 2013
  6. Andriy Mnih and Geoffrey Hinton. A Scalable Hierarchical Distributed Language Model. 2008
  7. Frederic Morin and Yoshua Bengio. Hierarchical probabilistic neural network language model. 2005
  8. Kishore Papineni et al. Bleu: a Method for Automatic Evaluation of Machine Translation. 2002