Natural Language Processing
Course Description
Theoretical fundamentals of natural language processing. Language collections: dictionaries and corpora, markup systems. Learning from the corpus: learning new words, solving ambiguity problems, language models. Grammar: Hidden Markov models (HMM), context-independent grammar (CFG) and others. Application of grammatical models in corpus markup and parsing. Linguistic pre-processing and post-processing in text synthesis. The impact of natural language processing applications on social development and language change. Methods and tools for machine translation.
Learning Outcomes
- identify computational complexity of NLP problems
- evaluate open source NLP tools
- manipulate text and speech corpora
- participate in speech synthesis projects
- participate in speech recognition projects
- participate in machine translation projects
Forms of Teaching
Lectures
Alive lectures
LaboratoryResearch Project Tasks
Week by Week Schedule
- Introduction to NLP, Language Modeling
- Computational morphology, computational semantics (formal semantics, semantic role labeling)
- Part of speech tagging, Hidden Markov Models
- Deterministic and stochastic grammars, constituency and dependency grammars (CFG, PCFG)
- Parsing algorithms (CYK, Chart), lexicalized parsing, dependency parsing
- Distributional semantic models and word embeddings
- Corpus-based methods, n-grams, collocations
- Language models, smoothing, evaluation
- Midterm exam
- Lexicons for Sentiment, Affect, and Connotation
- Graph based NLP: semantics; syntax and applications
- Neural Language Models
- Transfer Learning with Contextual Embeddings and Pre-trained language models
- Methods and tools for machine translation
- Final exam
Study Programmes
University graduate
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Literature
For students
General
ID 222553
Winter semester
5 ECTS
L0 English Level
L1 e-Learning
30 Lectures
0 Seminar
6 Exercises
15 Laboratory exercises
0 Project laboratory
Grading System
90 Excellent
80 Very Good
70 Good
50 Acceptable