Text Analysis and Retrieval is our flagship course. It brings together topics from natural language processing, information retrieval, and relevant machine learning techniques. The course aims to provide our students with an understanding of the theoretical foundations and applications of text analysis and retrieval methods, as well as best practices, trends, and challenges in these exciting and growing fields. Not only are these topics important for our students, but we’re extra thrilled to teach the tricks of our own trade.
The course gives a systematic overview of both traditional and advanced methods for text analysis and retrieval. The first part of the course deals with the basic natural language processing tasks, document representation and retrieval, as well as document classification and clustering. The second part deals with information extraction and text mining, with an extra emphasis on statistical natural language processing and machine learning methods.
Students who finish this course gain a working familiarity with basic natural language processing methods and an understanding of the main information retrieval and information extraction models, the theoretical foundations of these methods as well as their limitations, advantages, and disadvantages. They also learn about the relevant text analysis tools and frameworks.
An important part of the coursework is a final group project, in which our students design, implement, and evaluate a simple document retrieval or text analysis system.
Want to see how they did? Take a peek at last year’s projects reports here: Text Analyis and Retrieval 2014 – Course Project Reports
The course is offered in English only.
Browse the official course website at http://www.fer.unizg.hr/predmet/apt
Learn more about these and other courses offered at UNIZG FER in the UNIZG FER Course Catalogue (Graduate Study).