Czech Dataset for Semantic Similarity and Relatedness
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43949764" target="_blank" >RIV/49777513:23520/17:43949764 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.26615/978-954-452-049-6_053" target="_blank" >http://dx.doi.org/10.26615/978-954-452-049-6_053</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.26615/978-954-452-049-6_053" target="_blank" >10.26615/978-954-452-049-6_053</a>
Alternative languages
Result language
angličtina
Original language name
Czech Dataset for Semantic Similarity and Relatedness
Original language description
This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the words are ambiguous. The dataset is annotated by 5 independent annotators. The average Spearman correlation coefficient of the annotation agreement is r = 0.81. We provide reference evaluation experiments with several methods for computing semantic similarity and relatedness.
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
20204 - Robotics and automatic control
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)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Recent Advances in Natural Language Processing Meet Deep Learning
ISBN
978-954-452-048-9
ISSN
1313-8502
e-ISSN
neuvedeno
Number of pages
6
Pages from-to
401-406
Publisher name
INCOMA Ltd.
Place of publication
Shoumen, Bulgaria
Event location
Varna
Event date
Sep 2, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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