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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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