SiLi Index: Data Structure for Fast Vector Space Searching
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Proceedings of the Thirteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2019 |
| MU Faculty or unit | |
| Citation | |
| web | https://nlp.fi.muni.cz/raslan/2019/paper07-herman.pdf |
| Keywords | word embeddings; vector space; semantic similarity |
| Description | 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. |
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