Human Language Technologies

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  Schedule

  Day	Hour	Room
  Monday	11-13	L1, Polo Fibonacci
  Tuesday	9-11	C1, Polo Fibonacci
  Friday	14-16	A1, Polo Fibonacci

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  • Seleziona attività Homework 1

    

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• Seleziona sezione Lectures

  Lectures

  Date	Topic	Material
  18/2/2019	Introduction	slides
  19/2/2019	Language Models	slides Suggested readings: SLP, Chapter 3.
  22/2/2019	Language Models	slides Notebook
  25/2/2019	Word Vectors	slides Suggested readings: Word2Vec Tutorial
  26/2/2019	Word Vectors	slides
  27/2/2019	Tokenization	slides
  1/3/2019	Introduction to Python and NLTK	See notebooks: Introdution to Python Introduction to NLTK
  4/3/2019	Text Classification	slides Suggested readings: SLP Chapter 6
  5/3/2019	Text Classification	slides
  8/3/2019	Preprocessing text	slides Suggested readings: SLP Chapter 2
  11/3/2019	Hidden Markov Models	slides Suggested readings: SLP Chapter 9
  12/3/2019	Named Entity Recognition	slides Suggested readings: SLP Chapter 9
  15/3/2019	Deep Learning for NLP	slides Suggested readings: R. Collobert et al. 2011. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research
  18/3/2019	Deep Learning Libraries	slides See notebooks: MNIST/MNIST in Keras 2D.ipynb HLT/RNN-Tagger-keras.ipynb
  19/3/2019	Introduction to TensorFlow	slides See notebooks: TensorFlow-Examples/notebooks
  22/3/2019	Dependency Parsing	slides Further readings: Joakim Nivre. 2004. Incrementality in Deterministic Dependency Parsing. Workshop on Incremental Parsing. Danqi Chen and Christopher D. Manning. 2014. A Fast and Accurate Dependency Parser using Neural Networks. EMNLP 2014. Sandra Kübler, Ryan McDonald, Joakim Nivre. 2009. Dependency Parsing. Morgan and Claypool. Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016. Globally Normalized Transition-Based Neural Networks. ACL 2016. Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, and Christopher D. Manning. 2014. Universal Stanford Dependencies: A cross-linguistic typology. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014). Revised version for UD v1. Universal Dependencies website
  25/3/2019	Universal Dependencies	slides
  26/3/2019	Sentiment Analysis	slides
  29/3/2019	Sentiment Analysis: Lexical Resources	slides
  8/4/2019	Sentiment Analysis on Tweets: Semeval 2013-14	slides Further readings: NRC-SemEval2013.pdf NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
  9/4/2019	Aspect Based Sentiment Analysis on Tweets: SemEval 2015-17	slides Further readings: Understanding CNN for NLP UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification SwissCheese at SemEval 2016 Task 4
  12/4/2019	Recurrent Neural Networks	slides Further readings: Recurrent Neural Networks Tutorial The Unreasonable Effectiveness of Recurrent Neural Networks Sequence Modeling: Recurrent and Recursive Neural Nets Understanding LSTM Networks
  15/4/2019	Neural Networks with Memory	slides
  16/4/2019	Neural Networks with Memory and Attention	slides
  29/4/2019	Solution to Homework 1	See Jupyter Notebook HLT/HW1/Solution.ipynb See Jupyter Notebook HLT/HW1/POS Tagger with LSTM.ipynb
  30/4/2019	Neural Machine Translation	slides
  30/4/2018	Dynamic Neural Networks	slides
  3/5/2019	Deep Learning for Question Answering	slides Further readings: Neural Question Answering
  	Machine Translation	slides
  6/5/2019	Phrase Based Statistical Machine Translation	slides
  7/5/2019	The Transformer and BERT	slides
  10/5/2019	Chatbots	slides, Gunrock Alexa Prize 2018 winner Further readings: Inside Amazon’s $3.5 million competition to make Alexa chat like a human Gunrock: Building A Human-Like Social Bot By Leveraging Large Scale Real User Data IBM Project Debater
  13/5/2019	Reinforcement Learning	slides
  14/5/2018	Semi-supervised Learning for NLP	See Kevin Clark's presentation.
  17/5/2019	Coreference Resolution	slides
  20/5/2019	Multitask Learning (guest video lecture by Richard Socher)	video
  21/5/2019	Question Answering	slides
  24/5/2018	Future of NLP	slides
  27/5/2018	Canceled for European elections
• Seleziona sezione Textbooks

  Textbooks

  1. D. Jurafsky, J.H. Martin, Speech and Language Processing. 3rd edition, Prentice-Hall, 2018.
  2. S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.

  3. Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.

  Additional Material

  1. B. Liu, Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012.
     
  2. S. Kubler, R. McDonald, J. Nivre. Dependency Parsing. 2010.
     
  3. P. Koehn. Statistical Machine Translation. Cambridge University Press, 2010.
     

  
  
  

  
  
• Seleziona sezione Similar Courses

  Similar Courses

  • Deep Learning for NLP, Stanford, CS224n
  • Neural Networks for NLP, CMU, CS 11-747, Spring 2019
• Seleziona sezione Previous Editions

  Previous Editions

  • HLT 2018