How Many Pages? Paper Length Prediction from the Metadata
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424516" target="_blank" >RIV/00216208:11320/20:10424516 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3443279.3443305" target="_blank" >https://dl.acm.org/doi/10.1145/3443279.3443305</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3443279.3443305" target="_blank" >10.1145/3443279.3443305</a>
Alternative languages
Result language
angličtina
Original language name
How Many Pages? Paper Length Prediction from the Metadata
Original language description
Being able to predict the length of a scientific paper may be helpful in numerous situations. This work defines the paper length prediction task as a regression problem and reports several experimental results using popular machine learning models. We also create a huge dataset of publication metadata and the respective lengths in number of pages. The dataset will be freely available and is intended to foster research in this domain. As future work, we would like to explore more advanced regressors based on neural networks and big pretrained language models.
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/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
4th International Conference on Natural Language Processing and Information Retrieval
ISBN
978-1-4503-7760-7
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
91-95
Publisher name
ACM
Place of publication
New York, USA
Event location
Seoul, Korea
Event date
Dec 18, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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