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.

Basic information

Original name

Advanced learning techniques for NLP

Name in Czech

Pokročilé metody učení pro zpracování přirozeného jazyka

Name (in English)

Advanced learning techniques for NLP

Authors

POPELÍNSKÝ, Lubomír (203 Czech Republic, guarantor, belonging to the institution)

Edition

8th summer school of the European Masters in Language and Speech, 2007

---

Other information

Language

Czech

Type of outcome

Vyžádané přednášky

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

RIV identification code

RIV/00216224:14330/07:00041849

Organization unit

Faculty of Informatics

Keywords in English

machine learning; inductive logic programming; natural language processing

Tags

inductive logic programming, machine learning, natural language processing

Tags

International impact

---

Abstract

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

In English

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