|
After successful completion of this course, students have an understanding, both at the conceptual and the technical level, of the application of natural language processing (NLP) and multimedia processing techniques to the areas of text and multimedia mining. Students can build models for machine learning algorithms, and they can evaluate and report on the developed models. Also students understand, from a theoretical perspective, which tools are applicable in which situations, and which real-world challenges prevent the application of certain techniques (such as language variation and noise due to document processing errors). |
|
Text mining, also known as 'knowledge discovery from text', is an ICT research and development field that has gained increasing focus in the last decade, attracting researchers from computational linguistics, machine learning (an AI subfield), and information retrieval. Example key applications that have emerged from this melting pot are question answering, social media mining, and summarization. The emphasis of the course is on text data, but spoken audio, and other multimedia data are also covered. This course gives an overview of the field in a practical, hands-on fashion. In addition to the lectures, the students work on a self-chosen text mining problem in the second half of the course, resulting in a term paper.
|
|