Advanced learning techniques for NLP

POPELÍNSKÝ, Lubomír. Advanced learning techniques for NLP. In 8th summer school of the European Masters in Language and Speech. 2007.

Základní údaje

• Originální název: Advanced learning techniques for NLP
• Název česky: Pokročilé metody učení pro zpracování přirozeného jazyka
• Název anglicky: Advanced learning techniques for NLP
• Autoři: POPELÍNSKÝ, Lubomír (203 Česká republika, garant, domácí).
• Vydání: 8th summer school of the European Masters in Language and Speech, 2007.

Další údaje

• Originální jazyk: čeština
• Typ výsledku: Vyžádané přednášky
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Česká republika
• Utajení: není předmětem státního či obchodního tajemství
• Kód RIV: RIV/00216224:14330/07:00041849
• Organizační jednotka: Fakulta informatiky
• Klíčová slova anglicky: machine learning; inductive logic programming; natural language processing
• Štítky: inductive logic programming, machine learning, natural language processing
• Příznaky: Mezinárodní význam

Anotace

Inductive logic programing (ILP) aims at learning first-order predicate formula from positive and maybe negative examples. This learning technique is not limited to single-table data (like most of other learning method) and is especially suitable for data of complex structure. ILP has been successful in part-of-speech tagging (English, Swedish, Spanish, Czech), error detection in a morphologically tagged Czech corpus, in text categorization and information extraction. The aim of the tutorial is to provide the participants with practical usage of ILP for several NLP tasks. Summary A brief overview of ILP ILP for Part-of-Speech Tagging. A case studies: POS tagging for English; Error detection in a Czech corpus ILP for Text filtering and Information Extraction A case studies: Filtering situations and action from news reports; Learning agent-target from biomedical texts First-order frequent patterns and association rules for NLP

Anotace anglicky

Inductive logic programing (ILP) aims at learning first-order predicate formula from positive and maybe negative examples. This learning technique is not limited to single-table data (like most of other learning method) and is especially suitable for data of complex structure. ILP has been successful in part-of-speech tagging (English, Swedish, Spanish, Czech), error detection in a morphologically tagged Czech corpus, in text categorization and information extraction. The aim of the tutorial is to provide the participants with practical usage of ILP for several NLP tasks. Summary A brief overview of ILP ILP for Part-of-Speech Tagging. A case studies: POS tagging for English; Error detection in a Czech corpus ILP for Text filtering and Information Extraction A case studies: Filtering situations and action from news reports; Learning agent-target from biomedical texts First-order frequent patterns and association rules for NLP