Academic Year: 2017-2018

Advanced Natural Language Processing

and Information Retrieval (ANLP-IR)

Course presentation

Part1:

1. Basic Information Retrieval, Machine Learning Natural Language Processing (PDF)

Lab 2018

Format of Reports for the Projects

Additional Material

Information Retrieval Lectures

2. Kernel Methods for NLP (PDF)

3. Named Entity Recognition and POS-Tagging (PDF)

4. Syntactic Parsing (PDF)

5. Coreference and Anaphora Resolution (PDF)

- Motivation and presentation of the course + inverted index
- In depth on tokenization, normalization and optimization
- Preprocessing, data structures, n-grams and wildcards
- Vector Space Model and weighting schemes
- Efficient methods for document retrieval
- Performance Measures and Query Expansion

The above presentations are  based on the IR courses:
http://nlp.stanford.edu/IR-book/newslides.html

whereas the book (also adopted in my course) is available at:
http://nlp.stanford.edu/IR-book/


  Machine Learning Lectures

- Text Categorization and Feature Selection
- Statistical Learning Theory: linear classifiers
- Support Vector Machines
- Structured Output Spaces
- Kernel Methods


As referring text please use my new chapter:

Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning

along with the old book (with some typos)

Roberto Basil and Alessandro Moschitti, Automatic Text Categorization: from Information Retrieval to Support Vector Learning. Aracne editrice, Rome, Italy.

  Natural Language Processing Lectures

- POS-Tagging and Named Entity Recognition
- Syntactic Parsing
- Semantic Role Labeling
- UIMA Introduction
- Coreference Resolution
- Latent Semantic Analysis
- Kernel Methods for Natural Language Processing


 

Academic Year: 2015-2016

Advanced Natural Language Processing

and Information Retrieval (ANLP-IR)

Part1:

1. Basic Information Retrieval, Machine Learning Natural Language Processing (PDF)

2. Kernel Methods for NLP (PDF)

3. Named Entity Recognition and POS-Tagging (PDF)

4. Syntactic Parsing (PDF)

5. Coreference and Anaphora Resolution (PDF)

LAB1: Indexing, Word Features, Document and Term Frequency, Text Categorization)

Download:

TCF - Text Categorization Framework

LAB2: Support Vector Machines and Kernel Methods

Download:

(use the TCF above)

LAB2.b: Combining Tree Kernels for Question/Answer classification

Download:

LAB2.b.zip

LAB2.c: Smoothed Partial Tree Kernel for Question Classification

Download:

LAB2.c.zip

LAB3: Ranking with Tree Kernels

Download:

LAB3.zip

Projects 2015-2016

Format of Reports for the Projects

Additional Material

Information Retrieval Lectures

- Motivation and presentation of the course + inverted index
- In depth on tokenization, normalization and optimization
- Preprocessing, data structures, n-grams and wildcards
- Vector Space Model and weighting schemes
- Efficient methods for document retrieval
- Performance Measures and Query Expansion

The above presentations are  based on the IR courses:
http://nlp.stanford.edu/IR-book/newslides.html

whereas the book (also adopted in my course) is available at:
http://nlp.stanford.edu/IR-book/


  Machine Learning Lectures

- Text Categorization and Feature Selection
- Statistical Learning Theory: linear classifiers
- Support Vector Machines
- Structured Output Spaces
- Kernel Methods


As referring text please use my new chapter:

Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning

along with the old book (with some typos)

Roberto Basil and Alessandro Moschitti, Automatic Text Categorization: from Information Retrieval to Support Vector Learning. Aracne editrice, Rome, Italy.

  Natural Language Processing Lectures

- POS-Tagging and Named Entity Recognition
- Syntactic Parsing
- Semantic Role Labeling
- UIMA Introduction
- Coreference Resolution
- Latent Semantic Analysis
- Kernel Methods for Natural Language Processing


 

Academic Year: 2012-2013

- Presentatione del corso 

- Introduzione all'Informatica

- Introduzione alla programmazione

- Compilatori, interpeti, e introduzione al C

- Costrutti del Linguaggio C

- Array-Stringhe-Matrici-Preprocessore

- L'algebra dei calcolatori

- Tipi di dati avanzati

- Argomenti avanzati

- Struttura dei calcolatori

Materiale aggiuntivo

- Slides del corso (Prof. Bianchini)

- Altre slides recenti della Prof Bianchini

- Overflow

- Stack e Record di Attivazione

- Complessità Computazionale

Link alle lezioni di laboratorio

Computational Methods for Data Analysis

- Introduction to Machine Learning:  Decision Tree and Bayesian Classifiers

- Vector Space Learning

- Introduction to Statistical Learning Theory

- VC-dimension

- Perceptron

- Support Vector Machines

- Kernel Methods for Structured Data

Academic Year: 2011-2012

Computational Methods for Data Analysis

- Introduction to Machine Learning:  Decision Tree and Bayesian Classifiers

- Vector Space Learning

- Introduction to Statistical Learning Theory

- VC-dimension

- Perceptron

- Support Vector Machines

- Kernel Methods for Structured Data

As referring text please use my new chapter:

Kernel-Based Machines for Abstract and Easy Modeling of Automatic Learning

along with the old book (with some typos)

Roberto Basili and Alessandro Moschitti, Automatic Text Categorization: from Information Retrieval to Support Vector Learning. Aracne editrice, Rome, Italy.

PhD Course: Natural Language Processing in Watson

- Preparation to the Watson Tutorial

- IBM Watson Tutorial (not available yet)

Additionally, choose three lectures from the followings:

    - Introd. to Machine Learning:  Decision Tree and Bayesian Classifiers

    - Vector Space Learning

    - Perceptron + Support Vector Machines

    - Introduction to Statistical Learning Theory + VC-dimension

    - POS-Tagging and Named Entity Recognition

    - Syntactic Parsing

- Presentatione del corso 

- Introduzione all'Informatica

- Introduzione alla programmazione

- Compilatori, interpeti, e introduzione al C

- Costrutti del Linguaggio C

- Array-Stringhe-Matrici-Preprocessore

- L'algebra dei calcolatori

- Tipi di dati avanzati

- Argomenti avanzati

- Struttura dei calcolatori

Materiale aggiuntivo

- Slides del corso (Prof. Bianchini)

- Altre slides recenti della Prof Bianchini

- Overflow

- Stack e Record di Attivazione

- Complessità Computazionale

Link alle lezioni di laboratorio

Academic Year: 2010-2011

PhD course: Machine Learning (to be updated)

- Kernel Methods (advanced lecture)

- Kernel Engineering

Informatica Generale

Presentatione del corso 

Introduzione al Corso

Slides del corso (Prof. Bianchini)

Altre slides recenti della Prof Bianchini

Prima e seconda lezione  (prima, seconda)

Overflow

Stack and activation record

Computational Complexity

Machine Learning for Laurea Specialistica

and

Master on Human Language Technology and Interfaces

(Course on Machine Learning for Natural Language Processing and Information Retrieval)

- Introduction to Machine Learning

- PAC Learning

- VC-Dimension

Academic Year: 2009-2010

PhD course: Machine Learning 

- Kernel Methods (advanced lecture) 

- Kernel Engineering 

Informatica Generale

Presentatione del corso 

Introduzione al Corso

Slides del corso (Prof. Bianchini)

Altre slides recenti della Prof Bianchini

Prima e seconda lezione  (prima, seconda)

Overflow

Stack and activation record

Computational Complexity

Machine Learning for Laurea Specialistica

and

Master on Human Language Technology and Interfaces

Course on Machine Learning for Natural Language Processing and Information Retrieval

- Introduction to Machine Learning 

- Automated Text Categorization (practical machine learning)

- PAC Learning

- VC-Dimension

Academic Year: 2008-2009

PhD course: Machine Learning 

- Kernel Methods (advanced lecture) 

- Kernel Engineering

Informatica Generale

Presentatione del corso 

Introduzione al Corso

Slides del corso

Altre slides recenti della Prof Bianchini

Prima e seconda lezione  (prima, seconda)

Overflow

Stack and activation record

Computational Complexity

Master on Human Language Technology and Interfaces

Course on Machine Learning for Natural Language Processing and Information Retrieval

- Introduction to Machine Learning 

- PAC Learning

- Basic Concepts

- VC-Dimension

Laurea Specialistica

- Introduction to Machine Learning 

- PAC Learning

- VC-Dimension

- Automated Text Categorization
- Lab for Automated Text Categorization
- Perceptrons
- Support Vector Machines
- Kernel Methods
- Tree Kernels

Academic Year: 2007-2008

Master on Human Language Technology and Interfaces

Course on Machine Learning for Natural Language Processing and Information Retrieval

Informatica Generale I

Course Presentation  (PDF)

Link 1  (all slides)

Other More Recent Slides of Bianchini

Link 2  (prima, seconda)

Overflow

Informatica Generale II

From 10 to 15

Stack and activation record

Computational Complexity

Classes of Computational Complexity

Object Oriented Introduction

C++

Exrcise classes (INF.GEN I and II)