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

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

V originále

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

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