Prvořádové časté vzory v dolování v textu

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F05%3A00014356" target="_blank" >RIV/00216224:14330/05:00014356 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

First-order Frequent Patterns in Text Mining

Popis výsledku v původním jazyce

In this paper a universal framework for mining long first-order frequent patterns in text data is presented. It consists of RAP, an ILP system for mining maximal first-order frequent patterns, and two types of redefined background knowledge. Two methodsof using generated patterns for solving text mining tasks are described: propositionalization and CBA (class based association). A new variant of the CBA rule based classifier is proposed. The framework is used for solving three text mining tasks: information extraction from biomedical texts, context-sensitive text correction of English and morphological disambiguation of Czech. The distributed mining of frequent patterns is described and its influence on mining in text is discussed. It is shown that frequent patterns as new features for propositionalization usually provide better results than CBA.

Název v anglickém jazyce

First-order Frequent Patterns in Text Mining

Popis výsledku anglicky

In this paper a universal framework for mining long first-order frequent patterns in text data is presented. It consists of RAP, an ILP system for mining maximal first-order frequent patterns, and two types of redefined background knowledge. Two methodsof using generated patterns for solving text mining tasks are described: propositionalization and CBA (class based association). A new variant of the CBA rule based classifier is proposed. The framework is used for solving three text mining tasks: information extraction from biomedical texts, context-sensitive text correction of English and morphological disambiguation of Czech. The distributed mining of frequent patterns is described and its influence on mining in text is discussed. It is shown that frequent patterns as new features for propositionalization usually provide better results than CBA.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2005

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

EPIA'05, 12th Portuguese Conference on Artificial Intelligence

ISBN

0-7803-9365-1

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

344-350

Název nakladatele

Institute of Electrical and Electronics Engineers, Inc.

Místo vydání

Covilha, Portugal

Místo konání akce

Covilha, Portugal

Datum konání akce

5. 12. 2005

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—