Course objectives:
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The goal of the course is to explain the basic concepts and methodology of the automatic natural language processing (language modeling, part--of-speech tagging, machine translation, etc.). Students will be able to analyse simple tasks pertaining to this domain and design and implement corresponding algorithms.
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Requirements on student
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Seminar work - processing of a language corpus using an existing software for automatic language analysis, analysis of the results in a written report.
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Content
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1. Introduction, overview of the terminology.
2.-3. Levels of (automatic) language processing - orthography, morphology, syntax and semantics, machine translation.
4.-5. Language modeling (prediction of the most probable word sequences).
6.-7. Part-of-speech tagging.
8.-9. Syntactic analysis.
10.-11. Machine translation.
12.-13. Semantic analysis - representation of the sentence meaning in the form of tectogrammatical structures.
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Activities
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Fields of study
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Studentům je k dispozici kurz v Google Classroom se všemi podstatnými informacemi a materiály.
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Guarantors and lecturers
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Literature
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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65
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Preparation for an examination (30-60)
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40
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Preparation for comprehensive test (10-40)
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30
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Individual project (40)
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40
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Total
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175
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
používat základní operace s vektory a maticemi |
orientovat se v základních pojmech teorie pravděpodobnosti |
rozumět základním pojmům z lingvistiky |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
používat programovací jazyk Python na alespoň mírně pokročilé úrovni |
číst a porozumět odbornému textu v českém i anglickém jazyce |
ovládat obecné zásady práce s knihovnami jazyka Python |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
orientovat se v teorii formálních gramatik |
rozumět principům vektorové reprezentace dokumentů |
vysvětlit principy statistického strojového překladu |
Skills - skills resulting from the course: |
efektivně pracovat s knihovnami jazyka Python pro zpracování přirozeného jazyka |
implementovat jednoduché konečné automaty v knihovně OpenFST |
využít principy vektorové reprezentace dokumentů pro úlohy vyhledávání informací či detekce tématu |
Competences - competences resulting from the course: |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Skills - skills achieved by taking this course are verified by the following means: |
Seminar work |
Competences - competence achieved by taking this course are verified by the following means: |
Seminar work |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Individual study |
Skills - the following training methods are used to achieve the required skills: |
Interactive lecture |
Individual study |
Competences - the following training methods are used to achieve the required competences: |
Task-based study method |
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