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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
3. Semantic Search
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
27/10/2016 Maximum Entropy Models (slides)
28/10/2016 Hidden Markov Model (slides)
10/11/2016 Named Entity Recognition (slides)
11/11/2016 MEMM (slides)
17/11/2016 Perceptron, SVM (8-classifiers.ppt)
19/11/2016 Deep Learning Libraries keras-mnist-tutorial.zip
24/11/2016 Dependency Formalism (slides)
25/11/2016 Dependency Parsing (Transition Based) Topics for Seminars and Projects
1/12/2016 Dependency Parsing (Graph Based )
2/12/2016 Sentiment Analysis13-opinionmining.ppt
Deep Learning Deep Learning Tutorial at NAACL 2013 ML Course by Andrew Ng
Deep Learning for NLP DL and the DeepNL Library
15/12/2016 Machine Translation (MT) (PBMT)
PB SMT (Phrase Tables, Decoding, Evaluation)
16/12/2016 Universal Dependencies
State of the Art in Sentiment Analysis of Tweets NRC Canada at SemEval 2013
Relation Extraction 12-relextraction.ppt
The tsunami of Deep Learning over NLP
Andrew Ng, “Nuts and Bolts of Building Applications using Deep Learning” https://www.dropbox.com/s/dyjdq1prjbs8pmc/NIPS2016%20-%20Pages%202-6%20(1).pdf

# Textbooks

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.

# Exam

Project or seminar.

# Evalita 2015 Projects

• SentiPolc: Sentiment Polarity in Twitter. Primo classificato.
• NeelIt: Named Entity Recognition and Linking in Twitter. Primo classificato

# Affine courses

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

# Edizioni Precedenti

magistraleinformatica/eln/start.1482134904.txt.gz · Ultima modifica: 19/12/2016 alle 08:08 (7 anni fa) da Giuseppe Attardi