FI:IA161 NLP in practice - Course Information
IA161 Natural Language Processing in Practice
Faculty of InformaticsAutumn 2024
- Extent and Intensity
- 1/1/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
In-person direct teaching - Teacher(s)
- doc. RNDr. Aleš Horák, Ph.D. (lecturer)
RNDr. Zuzana Nevěřilová, Ph.D. (lecturer)
doc. Mgr. Pavel Rychlý, Ph.D. (lecturer)
RNDr. Miloš Jakubíček, Ph.D. (lecturer)
RNDr. Marek Medveď, Ph.D. (lecturer)
Mgr. Radoslav Sabol (lecturer)
RNDr. Vít Suchomel, Ph.D. (lecturer)
Mgr. Tomáš Foltýnek, Ph.D. (lecturer) - Guaranteed by
- doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics - Timetable
- Wed 25. 9. to Wed 18. 12. Wed 10:00–11:50 A219
- Prerequisites
- All students should have basic practical knowledge of programming in Python. Overview knowledge of the natural language processing field at the level of introductory courses such as IB030 Introduction to Natural Language Processing or PA153 Natural Language Processing is expected. The seminar is given in English. Task solutions can be in English, Czech or Slovak.
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Image Processing and Analysis (programme FI, N-VIZ)
- Bioinformatics and systems biology (programme FI, N-UIZD)
- Computer Games Development (programme FI, N-VIZ_A)
- Computer Graphics and Visualisation (programme FI, N-VIZ_A)
- Computer Networks and Communications (programme FI, N-PSKB_A)
- Computing Technology and Methodology (programme FI, D-INF_A)
- Cybersecurity Management (programme FI, N-RSSS_A)
- Digital Linguistics (programme FI, N-DL)
- Discrete algorithms and models (programme FI, N-TEI)
- Formal analysis of computer systems (programme FI, N-TEI)
- Fundamentals of Computer Science (programme FI, D-INF_A)
- Fundamentals of Computer Science (programme FI, D-INF)
- Graphic design (programme FI, N-VIZ)
- Graphic Design (programme FI, N-VIZ_A)
- Hardware Systems (programme FI, N-PSKB_A)
- Hardware systems (programme FI, N-PSKB)
- Image Processing and Analysis (programme FI, N-VIZ_A)
- Information security (programme FI, N-PSKB)
- Informatics (programme FI, B-INF) (3)
- Informatics in education (programme FI, B-IVV) (2)
- Information Security (programme FI, N-PSKB_A)
- Human-Computer Interaction (programme FI, N-IZU)
- Quantum and Other Nonclassical Computational Models (programme FI, N-TEI)
- Cybersecurity (programme FI, B-CS)
- Deployment and operations of software systems (programme FI, N-SWE)
- Design and development of software systems (programme FI, N-SWE)
- Computer graphics and visualisation (programme FI, N-VIZ)
- Computational Linguistics (programme FF, N-PLIN_) (3)
- Computer Networks and Communications (programme FI, N-PSKB)
- Usable Security (programme FI, N-IZU)
- Principles of programming languages (programme FI, N-TEI)
- Programming and development (programme FI, B-PVA)
- Cybersecurity management (programme FI, N-RSSS)
- Services development management (programme FI, N-RSSS)
- Software Systems Development Management (programme FI, N-RSSS)
- Services Development Management (programme FI, N-RSSS_A)
- Software Systems Development Management (programme FI, N-RSSS_A)
- Software systems (programme FI, N-PSKB)
- Machine learning and artificial intelligence (programme FI, N-UIZD)
- Computing Technology and Methodology (programme FI, D-INF)
- Teacher of Informatics and IT administrator (programme FI, N-UCI)
- Informatics for secondary school teachers (programme FI, N-UCI) (2)
- User Experience in Visual Informatics (programme FI, N-IZU)
- Computer Games Development (programme FI, N-VIZ)
- Processing and analysis of large-scale data (programme FI, N-UIZD)
- Natural language processing (programme FI, N-UIZD)
- Course objectives
- The course participants will have the opportunity to learn about, test and experiment with advanced techniques of natural language processing (NLP) and to develop an understanding of the limits of those techniques. The course aims to introduce current research issues, and to meet in practice with particular programming techniques used in language technology applications.
- Learning outcomes
- After studying the course, the students will be able to:
- explain a selected NLP problem and list its main aspects;
- implement a basic or intermediate application for complex tasks in language processing, typically for Czech, Slovak, or English;
- create data resources (models, test sets) for a selected NLP problem and evaluate their assets;
- compare selected available tools for complex NLP tasks and apply them to chosen data resources with possible adaptations to particular purposes. - Syllabus
- The presented NLP problems will concentrate on practical problems connected with processing human-produced textual data. Particular topics include:
- Opinion mining, sentiment analysis
- Machine translation
- Parsing of Czech: Between Rules and Statistics
- Named Entity Recognition
- Building Language Resources from the Web (effective crawling, boilerplate removal, tokenisation, near duplicates identification)
- Language modelling
- Topic identification, topic modelling
- Extracting structured information from text
- Automatic relation extraction (hypernyms, synonyms, ...)
- Adaptive electronic dictionaries
- Terminology identification (keywords, key phrases)
- Anaphora resolution
- Stylometry
- Automatic language corrections
- Literature
- Dan Jurafsky and James H. Martin. Speech and Language Processing (2020, 3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/
- J. Eisenstein, Introduction to Natural Language Processing (2019), MIT Press.
- https://www.aclweb.org/anthology/
- Teaching methods
- Each lecture consists of a one-hour lesson about the theoretical issues connected with a particular NLP problem, and a one-hour practical work in a computer laboratory devoted to the implementation, adaptation and evaluation of the presented techniques on real-world data.
- Assessment methods
- Solving tasks in the practical part of the lecture or in homeworks.
- Language of instruction
- English
- Further Comments
- Study Materials
The course is taught annually. - Teacher's information
- http://nlp.fi.muni.cz/NlpInPracticeCourse
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/autumn2024/IA161