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magistraleinformatica:eln:start

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# 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 on Piazza

# Prerequisites

1. Basic Probability and Statistics
2. Programming

# Syllabus

1. Introduction
1. History
2. Present and Future
3. NLP and the Web
2. Mathematical Background
1. Probability and Statistics
2. Language Model
3. Hidden Markov Model
4. Viterbi Algorithm
5. Generative vs Discriminative Models
3. Linguistic Essentials
1. Part of Speech and Morphology
2. Phrase structure
3. Collocations
4. n-gram Models
5. Word Sense Disambiguation
6. Word Embeddings
4. Preprocessing
1. Encoding
2. Regular Expressions
3. Segmentation
4. Tokenization
5. Normalization
5. Machine Learning Basics
6. Text Classification and Clustering
7. Tagging
1. Part of Speech
2. Named Entity
8. Sentence Structure
1. Constituency Parsing
2. Dependency Parsing
9. Semantic Analysis
10. Statistical Machine Translation
11. Deep Learning
12. Libraries
1. NLTK
2. Theano/Keras
3. Tensorflow
13. Applications
1. Opinion Mining
2. Entity Linking
3. Semantic Search
4. Question Answering
5. 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)

# Testi di riferimento

1. C. Manning, H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 2000.
2. D. Jurafsky, J.H. Martin, Speech and Language Processing. 2nd edition, Prentice-Hall, 2008.
3. S. Kubler, R. McDonald, J. Nivre. Dependency Parsing. 2010.
4. P. Koehn. Statistical Machine Translation. Cambridge University Press, 2010.
5. S. Bird, E. Klein, E. Loper. Natural Language Processing with Python.
6. I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.

# Modalità di esame

Progetto e orale.

# Corsi affini

1. Apprendimento Automatico: Fondamenti
2. Data Mining: fondamenti
3. Information Retrieval
4. Sistemi Basati sulla Conoscenza

# Edizioni Precedenti

magistraleinformatica/eln/start.1477208062.txt.gz · Ultima modifica: 23/10/2016 alle 07:34 (7 anni fa) da Giuseppe Attardi