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77575-01 - Seminar: Natural Language Processing (3 CP)
| Semester | fall semester 2025 |
| Course frequency | Irregular |
| Lecturers | Sepideh Alassi (sepideh.alassi@unibas.ch, Assessor) |
| Content | Course Outline: Day 1: Text as Data: Anatomy of text, preprocessing pipelines, and the statistical properties of language. Day 2: Word Meaning and Representation: Word embeddings, semantic similarity, and co-occurrence matrices. Day 3: Language Structures: N-grams, POS tagging, dependency parsing, and Named Entity Recognition (NER). You will also learn how to train a custom NER model on your own corpus and evaluate its performance. Day 4: Higher Level Semantics: Sentiment analysis and topic modelling. |
| Learning objectives | This course offers a practical introduction to Natural Language Processing (NLP) and demonstrates how its core concepts can be applied using Python. Each day is structured into two parts: morning sessions provide a theoretical overview of key topics, while afternoon sessions focus on practical examples and hands-on exercises. You will work with widely used Python libraries, including TensorFlow, spaCy, scikit-learn, Keras, and NLTK, to gain both conceptual understanding and practical skills. |
| Bibliography | - Jacob Eisenstein, Introduction to Natural Language Processing, The MIT Press, 2019. - Hobson Lane, Hannes Hapke, Cole Howard, Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python, Second Edition, Manning, 2025. |
| Comments | The course will be highly practical; therefore, attendance is mandatory. Please bring your own laptop with Python 3.12 installed. I also recommend installing the PyCharm IDE to facilitate programming. The Professional Edition is available free of charge for students—simply register with your UniBas email address on JetBrains. Download link: https://www.jetbrains.com/pycharm/download/ |
| Admission requirements | This course is intended for students with prior knowledge of Python, particularly those who have completed Introduction to Python for Humanities (course no. 64429-01). Students who have not taken this course but possess basic Python programming experience are encouraged to contact Dr. Sepideh Alassi to confirm that they meet the prerequisites. |
| Course application | The number of participants is limited. In case of over-subscription, students of Digital Humanities will be given priority. |
| Language of instruction | German |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 10.15-12.00 | -- Anfrage zentraler Raum für Lehre- |
Dates
| Modules |
Modul: Creating, Analyzing and Visualizing of Data (Master's degree subject: Digital Humanities) |
| Assessment format | continuous assessment |
| Assessment details | At the end of the course, students will begin working on a mini-project to be submitted by mid-January 2026, accompanied by a written report. Assessment will be based on the quality of the project. |
| Assessment registration/deregistration | Reg.: course registration; dereg.: not required |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Humanities and Social Sciences, studadmin-philhist@unibas.ch |
| Offered by | Digital Humanities Lab |