Neural Networks for Multi-lingual Multi-label 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%3A43952541" target="_blank" >RIV/49777513:23520/18:43952541 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-01418-6_8" target="_blank" >http://dx.doi.org/10.1007/978-3-030-01418-6_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-01418-6_8" target="_blank" >10.1007/978-3-030-01418-6_8</a>
Alternative languages
Result language
angličtina
Original language name
Neural Networks for Multi-lingual Multi-label Document Classification
Original language description
This paper proposes a novel approach for multi-lingual multilabel document classification based on neural networks. We use popular convolutional neural networks for this task with three different configurations. The first one uses static word2vec embeddings that are let as is, while the second one initializes it with word2vec and fine-tunes the embeddings while learning on the available data. The last method initializes embeddings randomly and then they are optimized to the classification task. The proposed method is evaluated on four languages, namely English, German, Spanish and Italian from the Reuters corpus. Experimental results show that the proposed approach is efficient and the best obtained F-measure reaches 84%.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: Research and Development of Intelligent Components of Advanced Technologies for the Pilsen Metropolitan Area (InteCom)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
Artificial Neural Networks and Machine Learning – ICANN 2018
ISBN
978-3-030-01417-9
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
11
Pages from-to
73-83
Publisher name
Springer
Place of publication
Cham
Event location
Rhodes, Greece
Event date
Oct 4, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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