Human-Centred Natural Language Processing
HC-NLP
(in German: Human-Centred Natural Language Processing )
Module-ID: FIN-INF-120494 |
| Link: | LSF |
| Responsibility: | Ernesto William De Luca |
| Lecturer: | Marco Polignano |
| Classes: |
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| Applicability in curriculum: | - M.Sc. INF: Informatik - M.Sc. INGINF: Informatik - M.Sc. WIF: Informatik - M.Sc. DKE: Learning Methods and Models for Data Science - M.Sc. DKE: Data Processing for Data Science - M.Sc. DE: Grundlagen Ingenieurwesen - M.Sc. DE: Human Factors - M.Sc. VC: Computer Science |
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Abbreviation HCNLP |
Credit Points 6 |
Semester Winter |
Term ab 1. |
Duration 1 Semester |
Language english |
Level Master |
Intended learning outcomes:
Constructive Alignment-konform zu überarbeiten
Content:
What is Human-Centered Natural Language Processing
Traditional Natural Language Processing: Rule-based and Count-based Models
Modern Natural Language Processing: Prediction-based Models
Language Engineering
Dataset Creation
Dataset Curation with Human Values in Mind
Human-Computer Interaction
Human-Centered Evaluation of NLP Systems
Human-Centered Design of NLP Systems
Human-Centered NLP Applications: Digital Humanities, Legal Artificial Intelligence, Recommender Systems
Human-AI Collaboration and Future Directions
Workload:
56 h contact time + 94h independent study + 30h project work
| Pre-examination requirements: | Type of examination: | Teaching method / lecture hours per week (SWS): |
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Written report (Hausarbeit) |
Lecture (3 SWS) Exercise (3 SWS) |
| Prerequisites according to examination regulations: | Recommended prerequisites: |
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keine |
| Media: | Literature: |
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Manning, C., & Schutze, H. (1999). Foundations of statistical natural language processing. MIT press. - Ziems, C., Yu, J. A., Wang, Y. C., Halevy, A., & Yang, D. (2022). The moral integrity corpus: A benchmark for ethical dialogue systems. arXiv preprint arXiv:2204.03021. - Niven, T., & Kao, H. Y. (2019). Probing neural network comprehension of natural language arguments. arXiv preprint arXiv:1907.07355. - Belz, A., Thomson, C., Reiter, E., Abercrombie, G., Alonso-Moral, J. M., Arvan, M., ... & Yang, D. (2023). Missing information, unresponsive authors, experimental flaws: The impossibility of assessing the reproducibility of previous human evaluations in NLP. arXiv preprint arXiv:2305.01633. - Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., ... & Weld, D. (2021, May). Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-16).
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