The Benefit of Document Embedding in Unsupervised Document Classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952604" target="_blank" >RIV/49777513:23520/18:43952604 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-319-99579-3_49" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-99579-3_49</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-99579-3_49" target="_blank" >10.1007/978-3-319-99579-3_49</a>
Alternative languages
Result language
angličtina
Original language name
The Benefit of Document Embedding in Unsupervised Document Classification
Original language description
The aim of this article is to show that the document embedding using the doc2vec algorithm can substantially improve the performance of the standard method for unsupervised document classification -- the K-means clustering. We have performed rather extensive set of experiments on one English and two Czech datasets and the results suggest that representing the documents using vectors generated by the doc2vec algorithm brings a consistent improvement across languages and datasets. The English dataset -- 20NewsGroups -- was processed in a way that allows direct comparison with the results of both supervised and unsupervised algorithms published previously. Such comparison is provided in the paper, together with the results of supervised classification achieved by the state-of-the-art SVM classifier.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Speech and Computer 20th International Conference, SPECOM 2018 Leipzig, Germany, September 18-22, 2018 Proceedings
ISBN
978-3-319-99578-6
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
9
Pages from-to
470-478
Publisher name
Springer
Place of publication
Cham
Event location
Leipzig, Germany
Event date
Sep 18, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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