Text Mining | Faculty of Technical Sciences

Subject: Text Mining (17.E2524)

Type of studies	Title
Master Academic Studies	Computing and Control Engineering (Year: 1, Semester: Summer)
Master Academic Studies	Software Engineering and Information Technologies (Year: 1, Semester: Summer)
Master Academic Studies	Artificial Intelligence and Machine Learning (Year: 1, Semester: Summer)
Master Academic Studies	Information and Analytics Engineering (Year: 1, Semester: Summer)

General information:

Category	Theoretical-methodological
Scientific or art field	Applied Computer Science and Informatics
ECTS	6

The aims of the course are: provide students with the knowledge of important concepts and techniques of text mining and information extraction; make students capable of applying text mining (and information extraction) methods, tools and techniques.

Students are acquainted with important concepts and techniques of text mining and information extraction and are capable of applying text mining (and information extraction) methods, tools and techniques.

Basic concepts and overview of the field of computational text analysis and information extraction. Pre-processing of the text. Lexical, syntactic and semantic analysis. The use of machine learning methods in the analysis of text: classification and clustering of textual documents. Probabilistic models for information extraction:Maximum Entropy Models,Hidden Markov Models, Conditional Random Fields. Rule-based information extraction. Automatic extraction of terms. Automatic extraction and semantic annotation of named entities in text. Automatic text summarisation. Systems for automatic answering questions. Visualization of text data. Information extraction from business reports. Automatic recognition of emotions and attitudes from text (opinion and sentiment mining). Information extraction in biology and medicine.

Teaching methods include lectures, laboratory classes, homework assignments, and consultations. Lectures involve presenting the course materials using the necessary didactic tools while encouraging the students to participate actively. Laboratory classes (exercises) are realized through assignments that can be done independently or with the help of teaching assistants, as well as through homework assignments.

Authors	Title	Year	Publisher	Language
Yoav Goldberg	Neural Network Methods in Natural Language Processing	2017	Morgan & Claypool Publishers	English
Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred Damerau	Text Mining: Predictive Methods for Analyzing Unstructured Information	2004	Springer	English
Benjamin Bengfort, Rebecca Bilbro, Tony Ojed	Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine	2018	O'Reilly Media	English
Li Deng, Yang Liu	Deep Learning in Natural Language Processing	2018	Springer	English
Ronen Feldman, James Sanger	The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data	2006	Cambridge University Press	English