On Injecting Entropy-Like Features into Deep Neural Networks for Content Relevance Assessment
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962859" target="_blank" >RIV/49777513:23520/21:43962859 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-90425-8_5" target="_blank" >http://dx.doi.org/10.1007/978-3-030-90425-8_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-90425-8_5" target="_blank" >10.1007/978-3-030-90425-8_5</a>
Alternative languages
Result language
angličtina
Original language name
On Injecting Entropy-Like Features into Deep Neural Networks for Content Relevance Assessment
Original language description
This paper describes in details an innovative technique of injection of a global (or generally largescale) quality measure into a deep neural network (DNN) in order to compensate for the tendency of DNNs to found the resulting classification virtually from a superposition of local neighbourhood transformations and projections. We used a state AQ1 probability-like feature as the global quality measure and injected it into a DNN-based classifier deployed in a specific task of determining which parts of a web page are of certain interest for further processing by NLP techniques. Our goal was to decompose web sites of various internet AQ2 discussion forums to useful content, i.e. the posts of users, and useless content, i.e. forum graphics, menus, banners, advertisements, etc.
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Theory and Practice of Natural Computing
ISBN
978-3-030-90424-1
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
10
Pages from-to
59-68
Publisher name
Springer
Place of publication
Cham
Event location
Tsukuba, japan
Event date
Dec 7, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000763099600005