Master-Praktikum - Maschinelles Lernen für Anwendungen der natürlichen Sprachverarbeitung (IN2106, IN4249)
Master Lab Course - Machine Learning for Natural Language Processing Applications (IN2106, IN4249)
Lehrveranstaltung 0000004166 im SS 2021
Basisdaten
| LV-Art | Praktikum |
|---|---|
| Umfang | 6 SWS |
| betreuende Organisation | Informatik 2 - Lehrstuhl für Sprachen und Beschreibungsstrukturen in der Informatik (Prof. Seidl) |
| Dozent(inn)en |
Gerhard Johann Hagerer Edoardo Mosca Maximilian Wich Monika Wintergerst Leitung/Koordination: Georg Groh |
| Termine |
1 einzelner oder verschobener Termin |
Zuordnung zu Modulen
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IN2106: Master-Praktikum / Advanced Practical Course
Dieses Modul ist in den folgenden Katalogen enthalten:- weitere Module aus anderen Fachrichtungen
weitere Informationen
Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).
| ergänzende Hinweise | People are increasingly using text-based forms of communication, including social media, to exchange impressions of their everyday life or to get ideas for their individual lifestyle. As a result, a large amount of text data is available online, containing a wealth of information on various topics. This results in new use cases, which have implications for research on machine learning in the field of natural language processing. To this end, recent advances in this technical field appear promising, as they mimic human understanding of text by capturing and extracting universal semantic knowledge from large text resources. Therefore, we offer appropriate research literature, based on which students use state-of-the-art methods. These potentially involve neural networks and include semantic embedding, attention mechanisms, transfer learning, clustering algorithms, and unsupervised and supervised approaches. The lab is divided into five different tracks in terms of content and organization: - Text mining for opinion research on social media - Explainable artificial intelligence for NLP models and applications - Dialogue systems and recipe ingredient substitution for virtual dietary advisors - Hate speech detection in text data from social media - Text summarization NLP models - Knowledge-grounded conversation systems Matching Verfahren via matching.in.tum.de. - Preliminary meeting: Fr, Feb 5, 14:00 in https://bbb.in.tum.de/geo-d2x-ujh - Deregistration deadline: 31 March 2021 Bewerbung und Preliminary-Meeting-Folien: https://wiki.tum.de/display/socialcomputing/Application+NLP+Lab+Course+SS2021 |
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| Links |
LV-Unterlagen E-Learning-Kurs (z. B. Moodle) ergänzende Veranstaltung TUMonline-Eintrag |