magistraleinformatica:eln:start
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Indice
Elaborazione del Linguaggio Naturale
Laurea Magistrale: Informatica.
Docente: Giuseppe Attardi Ricevimento: Mercoledì, 11:00
Schedule | ||
---|---|---|
Day | Hour | Room |
Tuesday | 9-11 | C, Polo Fibonacci |
Friday | 14-16 | C, Polo Fibonacci |
Prerequisiti
- Calcolo delle probabilità e statistica
- Programmazione
Programma
- 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
- Preprocessing
- Encoding
- Regular Expressions
- Segmentation
- Tokenization
- Normalization
- NLTK
- Introduction to Python
- Overvies of NLTK libraries
- Classification
- Machine Learning
- Statistical classifiers
- Bayesan Network
- Perceptron
- Maximum Entropy
- Support Vector Machines
- Hidden Variable Models
- Clustering
- K-means
- Factored Models
- Singular Value Decomposition
- Latent Semantic Indexing
- Tagging
- Part of Speech
- Named Entity
- Super Senses
- Sentence Structure
- Constituency Parsing
- Dependency Parsing
- Semantic Analysis
- Semantic Role Labeling
- Coreference resolution
- Statistical Machine Translation
- Word-Based Models
- Phrase-Based Models
- Decoding
- Syntax-Based SMT
- Evaluation metrics
- Processing Pipelines
- Integrated tooolkit
- Frameworks
- Gate
- UIMA
- Data Pipeline
- Tanl
- Applications
- Information Extraction
- Information Filtering
- Recommender System
- Opinion Mining
- Semantic Search
- Question Answering
- Text Entailment
Lecture Notes
Date | Lecture | Notes |
---|---|---|
21/2/2014 | L'età della parola | |
25/2/2014 | Introduction 1-intro.pptx | |
28/2/2014 | Introduction to probability (slides) | |
4/3/2014 | Python Tutorial (slides) Python: Functionals and Generators | |
7/3/2014 | Text Classification (slides) | |
11/3/2014 | Naive Bayes Classifier | |
14/3/2014 | Introduction to NLTK (slides) | |
18/3/2014 | Segmentation and Tokenization (slides) | Homework 1 |
21/3/2014 | Maximum Entropy Models (slides) | Homework n. 2 |
25/3/2014 | Hidden Markov Model (slides) | |
28/3/2014 | Named Entity Recognition (slides) | |
8/4/2014 | Perceptron, SVM8-classifiers.ppt | |
11/4/2014 | Dependency Formalism(slides) | |
15/4/2014 | Dependency Parsing (Graph Based , Transition Based) | |
29/4/2014 | Relation Extraction 12-relextraction.ppt | |
2/5/2014 | Sentiment Analysis13-opinionmining.ppt | |
6/5/2014 | State of the Art in Sentiment Analysis of Tweets NRC Canada at SemEval 2013 | |
9/5/2014 | Deep Learning Deep Learning Tutorial at NAACL 2013 | |
13/5/2014 | Deep Learning for Sentiment Analysis | |
16/5/2014 | Machine Translation (MT) | |
20/5/2014 | Phrase Based Statistical Machine Translation (PBMT) | |
Summarization Summarization | ||
Automatic Speech Recognition ASR Overview |
Temi di Approfondimento
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.
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.1400671809.txt.gz · Ultima modifica: 21/05/2014 alle 11:30 (11 anni fa) da Giuseppe Attardi