Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43910950" target="_blank" >RIV/62156489:43110/17:43910950 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.4018/978-1-5225-1759-7.ch081" target="_blank" >http://dx.doi.org/10.4018/978-1-5225-1759-7.ch081</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-5225-1759-7.ch081" target="_blank" >10.4018/978-1-5225-1759-7.ch081</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities

Popis výsledku v původním jazyce

The chapter introduces clustering as a family of algorithms that can be successfully used to organize text documents into groups without prior knowledge of these groups. The chapter also demonstrates using unsupervised clustering to group large amount of unlabeled textual data (customer reviews written informally in five natural languages) so it can be used later for further analysis. The attention is paid to the process of selecting clustering algorithms, their parameters, methods of data preprocessing, and to the methods of evaluating the results by a human expert with an assistance of computers, too. The feasibility has been demonstrated by a number of experiments with external evaluation using known labels and expert validation with an assistance of a computer. It has been found that it is possible to apply the same procedures, including clustering, cluster validation, and detection of topics and significant words for different natural languages with satisfactory results.

Název v anglickém jazyce

Revealing Groups of Semantically Close Textual Documents by Clustering: Problems and Possibilities

Popis výsledku anglicky

The chapter introduces clustering as a family of algorithms that can be successfully used to organize text documents into groups without prior knowledge of these groups. The chapter also demonstrates using unsupervised clustering to group large amount of unlabeled textual data (customer reviews written informally in five natural languages) so it can be used later for further analysis. The attention is paid to the process of selecting clustering algorithms, their parameters, methods of data preprocessing, and to the methods of evaluating the results by a human expert with an assistance of computers, too. The feasibility has been demonstrated by a number of experiments with external evaluation using known labels and expert validation with an assistance of a computer. It has been found that it is possible to apply the same procedures, including clustering, cluster validation, and detection of topics and significant words for different natural languages with satisfactory results.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications

ISBN

978-1-5225-1759-7

Počet stran výsledku

40

Strana od-do

1981-2020

Počet stran knihy

3048

Název nakladatele

IGI Global

Místo vydání

Hershey

Kód UT WoS kapitoly

—