Human Language Technologies

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  Online classes are presented through Hangouts Meet at this link:
  https://meet.google.com/cxj-udcs-hwa

  
  

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  Schedule

  Day	Hour	Room
  Monday	14-16	C1, Polo Fibonacci
  Thursday	9-11	C1, Polo Fibonacci
  Friday	9-16	C1, Polo Fibonacci

  Jupyter Notebook Server

  A server is available for running Jupyter Notebooks. You can log into the server using your University credentials (do not use your private Gmail account).

  Jupyterlab is now integrated with GithHub, so that it can access directly repositories on GitHub.
  You will see a GitHub tab on the left. Set the name to Unipisa and folder HLT, which corresponds to repository:

      https://github.com/Unipisa/HLT

  

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    Discussion Forum
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    Projects Page
  • Select activity Homework 1

    

    Homework 1 Assignment
• Select section Lectures

  Lectures

  Date	Topic	Material
  17/2/2020	Introduction	slides
  20/2/2020	Language Models	slides Suggested readings: SLP, Chapter 3.
  21/2/2020	Language Models	slides Notebook
  24/2/2020	Word Vectors	slides Suggested readings: Word2Vec Tutorial
  27/2/2020	Word Vectors	slides
  28/2/2020	Tokenization	slides
  2/3/2020	Text Classification	slides Suggested readings: SLP Chapter 6
  5/3/2020	Canceled because of coronavirus	Homework 1 published.
  6/3/2020	Canceled because of coronavirus
  9/3/2020	Neural Network Classification	slides See notebook: MLP Classifier
  12/3/2020	Experiments with Neural Network Classifiers	slides See notebook: MLP Classifier
  13/3/2020	Hidden Markov Models	slides (PDF) Suggested readings: SLP Chapter 9
  16/3/2020	Sequence Tagging	slides See notebook: Pos Tagger Suggested readings: SLP Chapter 9
  19/3/2020	Named Entity Recognition	slides Suggested readings: SLP Chapter 9
  20/3/2020	Solution to Homework 1
  23/3/2020	Parsing	slides Suggested readings: SLP Chapters 12, 13, 14 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.
  26/3/2020	Dependency Parsing and Universal Dependencies	slides Suggested readings: SLP Chapters 15 Further readings: 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
  27/3/2020	Sentiment Analysis	slides
  30/3/2020	Sentiment Analysis on Tweets: Semeval 2013-14	slides Further readings: NRC-SemEval2013.pdf NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
  2/4/2020	Convolutional Neural Network for NLP	slides See notebook: CNN for NLP
  	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
  3/4/2020	Introduction to TensorFlow	slides See notebooks: TensorFlow-Examples/notebooks Tensorflow Tutorials
  6/4/2020	Recurrent Neural Networks	slides Notebook: RNN POS Tagger Further readings: Recurrent Neural Networks Tutorial The Unreasonable Effectiveness of Recurrent Neural Networks Sequence Modeling: Recurrent and Recursive Neural Nets Understanding LSTM Networks
  9/4/2020	Suggestions and discussion about projects	slides
  	Sentiment Analysis: Lexical Resources	slides
  16/4/2020	Machine Translation	slides
  17/4/2020	Phrase Based Statistical Machine Translation	slides
  20/4/2020	Phrase Based Statistical Machine Translation (2)	slides
  23/4/2020	Neural Machine Translation	slides
  24/4/2020	The Transformer and BERT	slides
  27/4/2020	BERT	slides Notebook: Sentence Classification with BERT
  30/4/2020	Hugginface Transformers, Syntax Probes	slides Notebooks: 02-transformers.ipynb 03-pipeline.ipynb
  4/5/2020	Introduction to Pytorch	See notebooks: Introduction to Pytorch Pytorch Tutorial
  7/5/2020	Question Answering	slides
  8/5/2020	Reading Comprehension	slides
  11/5/2020	Neural Networks with Memory	slides
  14/5/2020	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
  15/5/2020	Chatbot Experiments	slides
  18/5/2020	Future of NLP	slides
  21/5/2020	Bonus: Thinking Fast and Slow	slides
• Select section Textbooks

  Textbooks

  1. D. Jurafsky, J.H. Martin, Speech and Language Processing. 3rd edition, Prentice-Hall, 2020.
  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.
     

  
  
  

  
  
• Select section Tutorials

  Tutorials

  • A free online course on Python for Machine Learning.
• Select section Similar Courses

  Similar Courses

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

  Previous Editions

  • HLT 2019
  • HLT 2018