Modulbeschreibung
Natural Language Processing
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Natural language processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. NLP systems can process and understand human language. The past decade has witnessed several breakthroughs in NLP which have resulted in an immense growth in the importance of NLP. Enabled by cloud computing, big data technologies and machine learning, NLP has clearly entered the mainstream as these technologies can now be applied to handle large volumes of text data at an unprecedented speed. NLP solutions deliver immense value for organizations across a wide range of different sectors, from digital communications to healthcare and medicine to finance, marketing, and retail. Some of the most common and proven applications of NLP in the industry today are:
- Spell checks (e.g. Grammarly)
- Chatbots
- Text classification
- Automatic summary generation
- Language translation
- Sentiment analysis
- Market intelligence
- Virtual assistance (e.g. Alexa and Siri)
- Automated language translation (e.g. Google Translate, Microsoft/Skype Translator)
The consequence of the enormous growth in NLP-based applications is that NLP is one of the 7 most in-demand tech skills to master in 2021. By 2025, the global NLP market is expected to reach over $34 billion. The logical implication is that universities urgently need to include NLP in the informatics and data science curriculums to optimally prepare their students for the job market and allow them to profit from the ample opportunities that arise across many industries. This holds especially true for Swiss Universities of Applied Sciences.
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Ebenfalls vorausgesetzt sind die beiden Module Elektrotechnik & Lineare Algebra I und Elektrotechnik & Lineare Algebra II.
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Das Modul findet im Frühlingssemester statt und wird Online angeboten.
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During the lecturing phase, a project has to be carried out individually which will be assessed by means of a written report and a technical discussion.
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Natural Language Processing
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Students who complete this introductory course will obtain a foundational understanding in effective techniques and toolkits for building real-world natural language processing applications. Throughout this course, the programming language Python is used to conduct textual and linguistic analyses. Students will gain a comprehensive view on natural language processing workflows —from data collection to extracting useful information from the data and then use that information to develop and deploy various NLP applications, such as topic modelling and text classification, via various machine learning techniques. We will also introduce language models and pay special attention to breakthrough deep learning powered language models, which have taken the NLP landscape by storm, outperforming the state-of-the-art across many tasks
Plan und Lerninhalt:
Natural language processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. NLP systems can process and understand human language. The past decade has witnessed several breakthroughs in NLP which have resulted in an immense growth in the importance of NLP. Enabled by cloud computing, big data technologies and machine learning, NLP has clearly entered the mainstream as these technologies can now be applied to handle large volumes of text data at an unprecedented speed. NLP solutions deliver immense value for organizations across a wide range of different sectors, from digital communications to healthcare and medicine to finance, marketing, and retail. Some of the most common and proven applications of NLP in the industry today are:
- Spell checks (e.g. Grammarly)
- Chatbots
- Text classification
- Automatic summary generation
- Language translation
- Sentiment analysis
- Market intelligence
- Virtual assistance (e.g. Alexa and Siri)
- Automated language translation (e.g. Google Translate, Microsoft/Skype Translator)
Ansprechspersonen:
Fachbereiche:
Unterrichtssprache:
Leistungsnachweis:
During the lecturing phase, a project has to be carried out individually which will be assessed by means of a written report and a technical discussion.
Lehr- und Lernmethoden:
lass discussion during classes
Self-study (exercises, preparation and follow-up of learning content)
Project work
Bibliographie:
- Steven Bird, Ewan Klein, and Edward Loper: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O'Reilly Media, Inc, 1st edition, July 21, 2009
- Uday Kamath, John Liu, and James Whitaker: Deep Learning for NLP and Speech Recognition, Springer; 1st ed. Edition, 2019
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Durchführung gemäss Stundenplan
Bemerkungen:
Die Unterrichtssprache ist englsich und deutsch