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@inproceedings{746494, author = {Popelínský, Lubomír and Materna, Jiří}, address = {Siena}, booktitle = {International conference Keyness in Text}, keywords = {keyness; machine learning; relational learning; inductive logic programming}, language = {eng}, location = {Siena}, pages = {61-63}, publisher = {University of Siena}, title = {Keyness and Relational Learning}, year = {2007} }
TY - JOUR ID - 746494 AU - Popelínský, Lubomír - Materna, Jiří PY - 2007 TI - Keyness and Relational Learning PB - University of Siena CY - Siena KW - keyness KW - machine learning KW - relational learning KW - inductive logic programming N2 - We propose a novel way to automatically extracting keyness concept of a document that exploits relational learning that allows to express not only keyness of a keyword but also the relation between keywords. It result in higher dicriminative power in information retrieval or text categorization. Informally, keyness is then a cloud of words and groups of words (clled keywords in the rest of this text), their attributes (morphological, syntactical, hyperon ymic) and relations between them that characterize a given document from the collection. ER -
POPELÍNSKÝ, Lubomír a Jiří MATERNA. Keyness and Relational Learning. In \textit{International conference Keyness in Text}. Siena: University of Siena, 2007, s.~61-63.
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