POPELÍNSKÝ, Lubomír. Advanced learning techniques for NLP. In 8th summer school of the European Masters in Language and Speech. 2007.
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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
Original language Czech
Type of outcome Requested lectures
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
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
Changed by Changed by: doc. RNDr. Lubomír Popelínský, Ph.D., učo 1945. Changed: 3/5/2011 06:50.
Abstract
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
Abstract (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
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