Text Mining

Questo Corso è parte del

Pathway in Introduction to Data Mining


Natural language text is everywhere, social networks, business, finance, medicine and biology are just few of the many sources of natural language text. However, computers are not fit to process natural language text. Indeed, Data Mining methods and algorithms, which operate on structured data, can not be directly applied to unstructured data for knowledge extraction. The Text Mining course, the last one of the Introduction to Data Mining pathway, introduces methods and tools for knowledge extraction from natural language text. The course assumes you are familiar with methods and models presented in the previous two courses, namely Data Mining: Classification and Data Mining: Clustering and Association. The course shows that Text Mining allows to formulate and solve problems in Business Intelligence, Finance, Recommendation, Medicine, Biomedicine, Social Networks, and Intelligence Gathering to mention just a few. In particular, the course introduces methods, models and algorithms for; natural language text preprocessing, text categorization, text clustering, topic modeling and information extraction.

Frequenza e Attestati

Frequenza
GRATUITO!
Attestato di Partecipazione
GRATUITO!

Categoria

Informatica, Gestione e Analisi dei Dati

Ore di Formazione

35

Livello

Base

Modalità Corso

Tutoraggio

Lingua

English

Durata

4 Settimane

Tipologia

Online

Stato del Corso

Tutoraggio Soft

Avvio Iscrizioni

31 Mar 2017

Apertura Corso

21 Apr 2017

Inizio Tutoraggio

21 Apr 2017

Fine Tutoraggio

29 May 2017

Tutoraggio Soft

30 May 2017

Chiusura Corso

Non impostato
By the end of this course, you will be able to develop, validate and apply Text Mining workflows for automatic classification and organization of natural language text. Furthermore, you will learn to develop workflows to extract entities (persons, organizations, locations, genes, drugs etc.) from natural language text and to discover their relationships. You will learn how to access natural language text from many sources such as RSS, Web pages, YouTube, Twitter, PubMed, PDF and txt files etc. 

The course is self-contained, and hands-on lectures exploit the KNIME open source software platform, which integrates power and expressiveness of Weka, R, Java, and Python.

Basic knowledge of probability and statistics. Basic knowledge of R programming. Data Mining: Classification, Data Mining: Clustering and Association.
  1. Sholom M. Weiss, Nitin Indurkhya and Tong Zhang (2010). Fundamentals of Predictive Text Mining, Springer. 
  2. Marie-Francine Moens (2006). Information Extraction: Algorithms and Prospects in a Retrieval Context, Springer.
The course spans four weeks. Each week requires 6 to 8 hours of work. Each week consists of 4 to 8 video-lectures. Each video-lecture consists of a methodology video, a software usage video and a practice session.
You must accomplish all practice sessions associated with lectures and upload the corresponding KNIME workflow to the course platform.

DANIELE BELLANI

DANIELE BELLANI

Department of Informatics, Systems and Communication

FABIO STELLA

FABIO STELLA

Department of Informatics, Systems and Communication

PAOLA CHIESA

PAOLA CHIESA

Department of Informatics, Systems and Communication

ALESSANDRO BREGOLI

ALESSANDRO BREGOLI

Department of Informatics, Systems and Communication