Title:

Natural Language Processing

Code:ZPD
Ac.Year:2010/2011
Term:Winter
Curriculums:
ProgrammeBranchYearDuty
CSE-PHD-4DVI4-Elective
Language:Czech
Completion:examination (verbal)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:390000
 ExaminationTestsExercisesLaboratoriesOther
Points:1000000
Guarantee:Smrž Pavel, doc. RNDr., Ph.D., DCGM
Lecturer:Smrž Pavel, doc. RNDr., Ph.D., DCGM
Faculty:Faculty of Information Technology BUT
Department:Department of Computer Graphics and Multimedia FIT BUT
 
Learning objectives:
  To understand natural language processing and to learn how to apply basic algorithms in this field. To get acquainted with the algorithmic description of the main language levels: morphology, syntax, semantics, and pragmatics, as well as the resources of natural language data - corpora. To conceive basics of knowledge representation, inference, and relations to the artificial intelligence.

 

Description:
  Foundations of the natural language processing, language data in corpora, levels of description: phonetics and phonology, morphology, syntax, semantics and pragmatics. Traditional vs. formal grammars: representation of morphological and syntactic structures, meaning representation. context-free grammars and their context-sensitive extensions, DCG (Definite Clause Grammars), CKY algorithm (Cocke-Kasami-Younger), chart-parsing. Problem of ambiguity. Electronic dictionaries: representation of lexical knowledge. Types of the machine readable dictionaries. Semantic representation of sentence meaning. The Compositionality Principle, composition of meaning. Semantic classification: valency frames, predicates, ontologies, transparent intensional logic (TIL) and its application to semantic analysis of sentences. Pragmatics: semantic and pragmatic nature of noun groups, discourse structure, deictic expressions, verbal and non-verbal contexts. Natural language understanding: semantic representation, inference and knowledge representations.
Learning outcomes and competences:
  Students will get acquainted with advanced methods of natural language processing. They will understand the algorithmic description of the main language levels: morphology, syntax, semantics, and pragmatics, as well as the resources of natural language data - corpora. By means of a self-study and a consultation, they will also grasp detailed knowledge of a selected part of the NLP field.
Syllabus of lectures:
 
  1. Advanced methods of  text categorization, document similarity
  2. Morphological analysis, inflective and derivational morphology, trie structure for dictionaries
  3. Methods of syntactic analysis for language modeling
  4. Probabilistic context-free analysis, automatic alignment, machine translation
  5. Lexical semantics, dictionaries vs. encyclopedias, compositionality
  6. The Semantic Web technologies, ontologies, OWL