Interdependence of text mining quality and the input data preprocessing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F15%3A43906660" target="_blank" >RIV/62156489:43110/15:43906660 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-18476-0_15" target="_blank" >http://dx.doi.org/10.1007/978-3-319-18476-0_15</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-18476-0_15" target="_blank" >10.1007/978-3-319-18476-0_15</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interdependence of text mining quality and the input data preprocessing

Popis výsledku v původním jazyce

The paper focuses on preprocessing techniques application to short informal textual documents created in different natural languages. The goal is to evaluate the impact on the quality of the results and computational complexity of the text mining processdesigned to reveal knowledge hidden in the data. Extensive number of experiments were carried out with real world text data with correction of spelling errors, stemming, stop words removal, and their combinations applied. Support vector machine, decision trees, and k-means algorithms as the commonly used methods were considered to analyze the text data. The text mining quality was generally not influenced significantly, however, the positive impact represented by the decreased computational complexitywas observed.

Název v anglickém jazyce

Interdependence of text mining quality and the input data preprocessing

Popis výsledku anglicky

The paper focuses on preprocessing techniques application to short informal textual documents created in different natural languages. The goal is to evaluate the impact on the quality of the results and computational complexity of the text mining processdesigned to reveal knowledge hidden in the data. Extensive number of experiments were carried out with real world text data with correction of spelling errors, stemming, stop words removal, and their combinations applied. Support vector machine, decision trees, and k-means algorithms as the commonly used methods were considered to analyze the text data. The text mining quality was generally not influenced significantly, however, the positive impact represented by the decreased computational complexitywas observed.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Intelligence Perspectives and Applications. Volume 1

ISBN

978-3-319-18475-3

ISSN

2194-5357

e-ISSN

—

Počet stran výsledku

10

Strana od-do

141-150

Název nakladatele

Springer Switzerland

Místo vydání

Cham

Místo konání akce

Zlín

Datum konání akce

27. 4. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000371407800015