Introduzione al CorsoNatural 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.
Computer and Data SciencesOre di Formazione
OnlineStato del Corso
Agenda del Corso
Risultati AttesiBy 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.
Pre-requisitiBasic knowledge of probability and statistics. Basic knowledge of R programming. Data Mining: Classification, Data Mining: Clustering and Association.
Libri di testo e letture consigliate
- Sholom M. Weiss, Nitin Indurkhya and Tong Zhang (2010). Fundamentals of Predictive Text Mining, Springer.
- Marie-Francine Moens (2006). Information Extraction: Algorithms and Prospects in a Retrieval Context, Springer.