Natural Language Processing and Understanding

Basic information

Workload: 

45 hours

Prerequisite: 

Does not exist

Syllabus: 

Introduction to NLP e NLU. Distributional Lexical Semantics. Lexical Semantics.
Sentiment analysis. Machine Learning methods applied to NLP and NLU. Natural Language Inference. Language modeling. Sentence understanding. Word embeddings.
 

Bibliography

Mandatory: 

·       Speech and Language Processing. Dan Jurafsky and Jim martin. 2014
·       Neural Network methods for Natural Language Processing. Yoav Goldberg. 2017
·       Foundations of Statistical Natural Language Processing. Manning and Schütze. 1999

Complementary: 

·       Python Natural Language Processing: Advanced machine learning and deep learning techniques for natural language processing. Jalaj Thanaki. 2017
·       Natural Language Processing with Python Cookbook: Over 60 recipes to implement text analytics solutions using deep learning principles. Krishna Bhavsar and Naresh Kumar. 2017
·       Deep Learning in Natural Language Processing. Li Deng and Yang Liu. 2018
·       Deep Learning with Text: Natural Language Processing (Almost) from Scratch with Python and spaCy. Patrick Harrison and Matthew Honnibal. 2018
·       Natural Language Understanding in a Semantic Web Context. Caroline Barrière. 2016.