TU München - Fakultät für
Seminar in the summer term 2021
Natural Language Processing (NLP) deals with the automatic analysis of natural language. Applications are for example the extraction of information from large amounts of text, automatic translation or automatic dialog systems. In recent years, the field has received a further boost through the emergence of deep-learning techniques, whereby numerous NLP tasks can now be solved with high quality. But also classical, proven NLP techniques are still in use.
In this seminar we want to look at some fields of application for NLP. The focus of the seminar is less the basics of NLP but more their application. Based on concrete data and tasks NLP techniques will be tried out and the result will be evaluated. In the seminar thisis and the seminar lecture the techniques as well as the result of the application will be presented. In addition to general use cases, possibilities of how NLP techniques can be used in the context of software or requirements engineering will be investigated.
Text-Classification and Extraction
Learning objectivesEach participant learns to work independently on a topic, to conduct literature research, to apply the knowledge gained and to write a paper on the given topic. Each participant gives a presentation on his or her topic in front of the other participants and supervisors and receives detailed feedback.
Success CriteriaAttendance at all appointments, preparation of own seminar paper, application of knowledge in a given case study and presentation.
PrerequisitesBasics in NLP / Machine Learning / Deep Learning (e.g. Natural Language Processing IN2361). Good (at least passive) knowledge of English, since the required literature is usually in English.
RegistrationRegistration details will be announced after the seminar briefing. Those interested in the seminar should send an application (status in studies, interest in software quality, motivation, which topic is desired?) to firstname.lastname@example.org by february, 11th.
OrganizerThe seminar is organized by