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@misc{755812, author = {Popelínský, Lubomír}, booktitle = {8th summer school of the European Masters in Language and Speech}, keywords = {machine learning; inductive logic programming; natural language processing}, language = {cze}, title = {Advanced learning techniques for NLP}, year = {2007} }
TY - SLIDE ID - 755812 AU - Popelínský, Lubomír PY - 2007 TI - Advanced learning techniques for NLP KW - machine learning KW - inductive logic programming KW - natural language processing N2 - 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 ER -
POPELÍNSKÝ, Lubomír. Advanced learning techniques for NLP. In \textit{8th summer school of the European Masters in Language and Speech}. 2007.
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