Information Extraction and Text Mining

Lectures: 24 h (seminars and lectures)

Recommended for

Graduate and doctoral students.

The course was last given: Never

The course runs: Fall 2001

Goals

To provide an overview of the field of Information Extraction, including concepts and methods that primarily has a Language Engineering perspective. The course will also cover fundamental issues in Text Mining and Document Summarisation.

Prerequisites

Graduate student, with some knowledge of linguistics.

Organization

Guded discussions based on reading of prepared material. Case studies and examples will be used to illustrate the practical application of the main concepts and methods.

Contents

IE can be seen as taking one step further compared to Information Retrieval (IR) in that not only the relevant documents should be found, but the process moves on to first single out passages, or extracts from documents, which contain the desired pieces of information, and second, turns them into structured information that is more readily digested and analyzed. IE requires a number of separate Language Engineering techniques that are used together; i.e., POS-tagging, functional and sense disambiguation, pronoun resolution, etc.

Literature

Carbonell, J.G., J. Siekmann & Maria T. Pazienza (eds.). Information Extraction : Towards Scalable, Adaptable Systems, Springer Verlag, Lecture Notes in Artificial Intelligence, 1999.

+ selected journal and conference articles.

Teachers

Magnus Merkel

Examiner

Magnus Merkel

Schedule

Fall 2001

Examination

Active participation (presenting articles) and written paper.

Credit

4 credits