– Introduction to Text Mining (NLP)
– Text Representation
– Language modeling
– Text classification
– neural network for Text
– Attention-based language modeling
https://neptun.elte.hu/MobilityCourses?Faculty=&Programme=&AcademicTerm=&Published=&SearchText=text+mining
At the end of the course, the learner will be able to understand how current technologies for data analysis and modelling operate and apply them to real-life scenarios, including those involving large volumes of data.
The learner will be familiar with techniques for storing, processing, and visualising large datasets, as well as with the characteristics of different tool ecosystems.
The learner will understand the main application areas of data science, the associated challenges, possible solutions, and the limitations of related methods and techniques.
The learner will be able to identify relationships between different types of data, extract meaningful information, and solve problems through data transformation in multidisciplinary contexts.
No specific pre-requisites are required for this course.
– D. Jurafsky, J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics (2nd ed.), Prentice-Hall, 2009.
– C. Manning and H. Schütze, Foundations of Statistical Natural Language Processing, MIT Press. Cambridge, MA: May 1999.
The course will combine lectures, practical demonstrations, hands-on exercises, case studies, and project-based learning. Teaching activities will focus on introducing key concepts, applying data analysis and modelling techniques to real-life examples, and encouraging learners to interpret results and solve problems using data. Individual and group activities may be included to support active learning, discussion, and multidisciplinary collaboration.
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