Elaborazione del Linguaggio Naturale

Laurea Magistrale: Informatica.

Docente: Giuseppe Attardi Ricevimento: Wednesday, 11:00

Lectures will start on September 28, 2015.

Prerequisiti

Introduction
1. History
2. Present and Future
3. NLP and the Web
Mathematical Background
1. Probability and Statistics
2. Language Model
3. Hidden Markov Model
4. Viterbi Algorithm
5. Generative vs Discriminative Models
Linguistic Essentials
1. Part of Speech and Morphology
2. Phrase structure
3. Collocations
4. n-gram Models
5. Word Sense Disambiguation
Preprocessing
1. Encoding
2. Regular Expressions
3. Segmentation
4. Tokenization
5. Normalization
NLTK
1. Introduction to Python
2. Overvies of NLTK libraries
Classification
1. Machine Learning
2. Statistical classifiers
  1. Bayesan Network
  2. Perceptron
  3. Maximum Entropy
  4. Support Vector Machines
  5. Hidden Variable Models
Clustering
1. K-means
2. Factored Models
  1. Singular Value Decomposition
  2. Latent Semantic Indexing
Tagging
1. Part of Speech
2. Named Entity
3. Super Senses
Sentence Structure
1. Constituency Parsing
2. Dependency Parsing
Semantic Analysis
1. Semantic Role Labeling
2. Coreference resolution
Statistical Machine Translation
1. Word-Based Models
2. Phrase-Based Models
3. Decoding
4. Syntax-Based SMT
5. Evaluation metrics
Processing Pipelines
1. Integrated tooolkit
2. Frameworks
  1. Gate
  2. UIMA
3. Data Pipeline
  1. Tanl
Applications
1. Information Extraction
2. Information Filtering
3. Recommender System
4. Opinion Mining
5. Semantic Search
6. Question Answering
  1. Text Entailment

Date	Lecture	Notes
	L'età della parola
28/9/2015	Introduction 1-intro.pptx
27/9/2015	Introduction to probability (Probability)
5/10/2015	Language Modeling (Language Modeling)
6/10/2015	Python Tutorial (Tutorial)
12/10/2015	Python Tutorial and examples (Python: Functionals and Generators)	Homework 1
13/10/2015	Introduction to NLTK (slides)
19/10/2015	Text Classification (slides)
20/10/2015	Naive Bayes Classifier	slides
26/10/2015	Segmentation and Tokenization (slides)
27/10/2015	Maximum Entropy Models (slides)	Homework n. 2
2/11/2015	Hidden Markov Model (slides)
3/11/2015	Named Entity Recognition (slides)
9/11/2015	MEMM (slides)
10/11/2015	Perceptron, SVM (8-classifiers.ppt)
16/11/2015	Dependency Formalism (slides)
17/11/2015	Dependency Parsing (Transition Based)	Topics for Seminars and Projects
23/11/2015	Dependency Parsing (Graph Based )
24/11/2015	Relation Extraction 12-relextraction.ppt
28/4/2015	Sentiment Analysis13-opinionmining.ppt
	State of the Art in Sentiment Analysis of Tweets NRC Canada at SemEval 2013
30/11/2015	Deep Learning Deep Learning Tutorial at NAACL 2013
1/12/2015	Deep Learning for NLP	DL and the DeepNL Library
14/12/2015	Machine Translation (MT)
15/12/2015	Phrase Based Statistical Machine Translation (PBMT)
15/12/2015	The tsunami of Deep Learning over NLP
	PB SMT (Phrase Tables, Decoding, Evaluation)

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.

Progetto e orale.