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 | 9-11 | L1, 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 |
---|---|---|
20/2/2012 | Introduction | |
21/2/2012 | Introduction to probability (slides) | |
27/2/2012 | Python Tutorial (slides) Python: Functionals and Generators | |
28/2/2012 | Text Classification (slides) | |
5/3/2012 | Naive Bayes Classifier | |
Introduction to NLTK (slides) | ||
6/3/2012 | Segmentation and Tokenization (slides) | Homework 1 |
27/3/2012 | Maximum Entropy Models (slides) | Homework n. 2 |
Hidden Markov Model (slides) | ||
Named Entity Recognition (slides) | ||
Perceptron, SVM | ||
17/4/2012 | Dependency Formalism(slides) | |
23/4/2012 | Dependency Parsing (Graph Based , Transition Based) | |
MEMM | ||
Machine Translation (MT) | ||
Phrase Based Statistical Machine Translation (PBMT) | ||
21/5/2013 | Deep Leraning | Deep Learning for NLP |
28/5/2013 | Summarization | Summarization |
28/5/2013 | 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.
Corsi affini
- Apprendimento Automatico: Fondamenti
- Data Mining: fondamenti
- Information Retrieval
- Sistemi Basati sulla Conoscenza
Edizioni Precedenti
magistraleinformatica/eln/start.1370280570.txt.gz · Ultima modifica: 03/06/2013 alle 17:29 (11 anni fa) da Giuseppe Attardi