Unsupervised Document Classification and Topic Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932650" target="_blank" >RIV/49777513:23520/17:43932650 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-66429-3_75" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-66429-3_75</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-66429-3_75" target="_blank" >10.1007/978-3-319-66429-3_75</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Document Classification and Topic Detection

Popis výsledku v původním jazyce

This article presents a method for pre-processing the feature vectors representing text documents that are consequently classified using unsupervised methods. The main goal is to show that state-of-the-art classification methods can be improved by a certain data preparation process. The first method is a standard K-means clustering and the second Latent Dirichlet allocation (LDA) method. Both are widely used in text processing. The mentioned algorithms are applied to two data sets in two different languages. First of them, the 20NewsGroup is a widely used benchmark for classification of English documents. The second set was selected from the large body of Czech news articles and was used mainly to compare the performance of the tested methods also for the case of less frequently studied language. Furthermore, the unsupervised methods are also compared with the supervised ones in order to (in some sense) ascertain the upper-bound of the task.

Název v anglickém jazyce

Unsupervised Document Classification and Topic Detection

Popis výsledku anglicky

This article presents a method for pre-processing the feature vectors representing text documents that are consequently classified using unsupervised methods. The main goal is to show that state-of-the-art classification methods can be improved by a certain data preparation process. The first method is a standard K-means clustering and the second Latent Dirichlet allocation (LDA) method. Both are widely used in text processing. The mentioned algorithms are applied to two data sets in two different languages. First of them, the 20NewsGroup is a widely used benchmark for classification of English documents. The second set was selected from the large body of Czech news articles and was used mainly to compare the performance of the tested methods also for the case of less frequently studied language. Furthermore, the unsupervised methods are also compared with the supervised ones in order to (in some sense) ascertain the upper-bound of the task.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/DG16P02B048" target="_blank" >DG16P02B048: Systém pro trvalé uchování dokumentace a prezentaci historických pramenů z období totalitních režimů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Speech and Computer 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings

ISBN

978-3-319-66428-6

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

9

Strana od-do

748-756

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Hatfield, Hertfordshire, United Kingdom

Datum konání akce

12. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—