Section: Lectures | Human Language Technologies - 2018 | INF - e-learning

Section outline

Date	Topic	Material
18/2/2019	Introduction	slides
20/2/2018	Language Models	slides Suggested readings: SLP, Chapter 3.
23/2/2018	Language Models	slides
26/2/2018	Word Vectors	slides Suggested readings: Word2Vec Tutorial
27/2/2018	Word Vectors	slides
4/3/2018	Text Classification	slides Suggested readings: SLP Chapter 6
5/3/2018	Tokenization	slides
1/3/2018	Introduction to Python and NLTK	See notebooks: Introdution to Python Introduction to NLTK
9/3/2018	Preprocessing text	slides Suggested readings: SLP Chapter 2
12/3/2018	Hidden Markov Models	slides Suggested readings: SLP Chapter 9
16/3/2018	Deep Learning for NLP	slides Suggested readings: R. Collobert et al. 2011. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research
19/3/2018	Keras	slides See notebooks: MNIST/MNIST%20in%20Keras.ipynb HLT/RNN-Tagger-keras.ipyn
22/3/2018	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
25/3/2018	Neural Dependency Parsing
29/3/2018	Universal Dependencies	slides
26/3/2018	Sentiment Analysis	slides
27/3/2018	Sentiment Analysis: Lexical Resources	slides
13/4/2018	Sentiment Analysis on Tweets: Semeval 2013-14	slides Further readings: NRC-SemEval2013.pdf NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews
16/4/2018	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
17/4/2018	Solution to Homework 1	See Jupyter Notebook HLT/HW1/Solution.ipynb
20/4/2018	Solution to Homework 1 POS Tagging, Parsing	See Jupyter Notebook HLT/HW1/Solution.ipynb
20/4/2018	Recurrent Neural Networks	slides See Jupyter Notebook HLT/HW1/POS Tagger with LSTM.ipynb Further readings: Recurrent Neural Networks Tutorial The Unreasonable Effectiveness of Recurrent Neural Networks Sequence Modeling: Recurrent and Recursive Neural Nets Understanding LSTM Networks
24/4/2018	Neural Networks with Memory	slides
27/4/2018	Neural Networks with Memory and Attention	slides
30/4/2018	Dynamic Neural Networks	slides
2/5/2018	Deep Learning for Question Answering	slides Further readings: Neural Question Answering
4/5/2018	Machine Translation	slides
7/5/2018	Phrase Based Statistical Machine Translation	slides
8/5/2018	Neural Machine Translation	slides
11/5/2018	Introduction to TensorFlow	slides
14/5/2018	Coreference Resolution	slides
15/5/2018	Project Suggestions	slides
18/5/2018	Chatbots	slides Further readings: Inside Amazon’s $3.5 million competition to make Alexa chat like a human IBM Project Debater
21/5/2018	Reinforcement Learning	slides
22/5/2018	Semi-supervised Learning for NLP	See Kevin Clark's presentation.
25/5/2018	Future of NLP	slides
28/5/2018	Discussion and progress report on projects

Section outline

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Topic

Material