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Indice
Elaborazione del Linguaggio Naturale
Laurea Magistrale: Informatica.
Docente: Giuseppe Attardi Ricevimento: Friday, 11:00
| Schedule | ||
|---|---|---|
| Day | Hour | Room |
| Thursday | 11-13 | N1, Polo Fibonacci |
| Friday | 16-18 | N1, Polo Fibonacci |
Forum
Forum on Piazza
Prerequisites
- Basic Probability and Statistics
- Programming
Syllabus
- Introduction
- History
- Present and Future
- NLP and the Web
- Mathematical Background
- Probability and Statistics
- Language Model
- Hidden Markov Model
- Viterbi Algorithm
- Generative vs Discriminative Models
- Linguistic Essentials
- Part of Speech and Morphology
- Phrase structure
- Collocations
- n-gram Models
- Word Sense Disambiguation
- Word Embeddings
- Preprocessing
- Encoding
- Regular Expressions
- Segmentation
- Tokenization
- Normalization
- Machine Learning Basics
- Text Classification and Clustering
- Tagging
- Part of Speech
- Named Entity
- Sentence Structure
- Constituency Parsing
- Dependency Parsing
- Semantic Analysis
- Statistical Machine Translation
- Deep Learning
- Libraries
- NLTK
- Theano/Keras
- Tensorflow
- Applications
- Opinion Mining
- Entity Linking
- Semantic Search
- Question Answering
- Language Inference
Lecture Notes
| Date | Lecture | Notes |
|---|---|---|
| 22/9/2016 | L'età della parola | L'età della parola |
| 23/9/2016 | Introduction | Introduction |
| 29/9/2016 | Introduction to probability (Probability) | |
| 30/9/2016 | Language Modeling (Language Modeling) | Jupyter Notebook |
| 6/10/2016 | Python Tutorial (Tutorial) | Python Tutorial Notebook |
| 7/10/2016 | Python Tutorial and examples (Python: Functionals and Generators) | Homework 1 |
| 13/10/2016 | Introduction to NLTK (slides) | |
| 14/10/2016 | Segmentation and Tokenization (slides) | |
| 20/10/2016 | Text Classification (slides) | |
| 21/10/2016 | Naive Bayes Classifier (slides) | Homework n. 2 |
| Maximum Entropy Models (slides) | ||
| Hidden Markov Model (slides) | ||
| Named Entity Recognition (slides) | ||
| MEMM (slides) | ||
| Perceptron, SVM (8-classifiers.ppt) | ||
| Dependency Formalism (slides) | ||
| Dependency Parsing (Transition Based) | Topics for Seminars and Projects | |
| Dependency Parsing (Graph Based ) | ||
| Relation Extraction 12-relextraction.ppt | ||
| Sentiment Analysis13-opinionmining.ppt | ||
| State of the Art in Sentiment Analysis of Tweets NRC Canada at SemEval 2013 | ||
| Deep Learning Deep Learning Tutorial at NAACL 2013 | ML Course by Andrew Ng | |
| Deep Learning for NLP DL and the DeepNL Library | ||
| Machine Translation (MT) | ||
| Phrase Based Statistical Machine Translation (PBMT) | ||
| The tsunami of Deep Learning over NLP | ||
| PB SMT (Phrase Tables, Decoding, Evaluation) |
Suggerimenti per Progetti o Seminari
Testi di riferimento
- C. Manning, H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 2000.
- D. Jurafsky, J.H. Martin, Speech and Language Processing. 2nd edition, Prentice-Hall, 2008.
- S. Kubler, R. McDonald, J. Nivre. Dependency Parsing. 2010.
- P. Koehn. Statistical Machine Translation. Cambridge University Press, 2010.
- S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.
- M. Nielsen. Neural Networks and Deep Learning.
Modalità di esame
Progetto e orale.
Progetti Evalita 2014
- Raccolta di tweet italiani tokenizzati.
- Raccolta di tweet italiani taggati con POS.
Corsi affini
- Apprendimento Automatico: Fondamenti
- Data Mining: fondamenti
- Information Retrieval
- Sistemi Basati sulla Conoscenza
Edizioni Precedenti
magistraleinformatica/eln/start.1477208062.txt.gz · Ultima modifica: 23/10/2016 alle 07:34 (8 anni fa) da Giuseppe Attardi
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