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@inproceedings{1591220,
author = {Herman, Ondřej and Rychlý, Pavel},
address = {Brno},
booktitle = {Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019},
editor = {Horák, Aleš and Rychlý, Pavel and Rambousek, Adam},
keywords = {word embeddings; vector space; semantic similarity},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-263-1530-8},
pages = {111-116},
publisher = {Tribun EU},
title = {SiLi Index: Data Structure for Fast Vector Space Searching},
url = {https://nlp.fi.muni.cz/raslan/2019/paper07-herman.pdf},
year = {2019}
}
TY - JOUR
ID - 1591220
AU - Herman, Ondřej - Rychlý, Pavel
PY - 2019
TI - SiLi Index: Data Structure for Fast Vector Space Searching
PB - Tribun EU
CY - Brno
SN - 9788026315308
KW - word embeddings
KW - vector space
KW - semantic similarity
UR - https://nlp.fi.muni.cz/raslan/2019/paper07-herman.pdf
N2 - Nearest neighbor queries in high-dimensional spaces are ex-pensive. In this article, we propose a method of building and querying astand-alone data structure, SiLi (SimilarityList) Index, which supports ap-proximating the results of k-NN queries in high-dimensional spaces, whileusing a significantly reduced amount of system memory and processortime compared to the usual brute-force search methods.
ER -
HERMAN, Ondřej a Pavel RYCHLÝ. SiLi Index: Data Structure for Fast Vector Space Searching. In Horák, Aleš and Rychlý, Pavel and Rambousek, Adam. \textit{Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019}. Brno: Tribun EU, 2019, s.~111-116. ISBN~978-80-263-1530-8.
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