Flexible Similarity Search of Semantic Vectors Using Fulltext Search Engines

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F03892620%3A_____%2F17%3AN0000002" target="_blank" >RIV/03892620:_____/17:N0000002 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14330/17:00094375
Result on the web
<a href="http://ceur-ws.org/Vol-1923/article-01.pdf" target="_blank" >http://ceur-ws.org/Vol-1923/article-01.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Flexible Similarity Search of Semantic Vectors Using Fulltext Search Engines
Original language description
Vector representations and vector space modeling (VSM) play a central role in modern machine learning. In our recent research we proposed a novel approach to ‘vector similarity searching’ over dense semantic vector representations. This approach can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. In this paper we validate our method using varied datasets ranging from text representations and embeddings (LSA, doc2vec, GloVe) to SIFT descriptors of image data. We show how our approach handles the indexing and querying in these domains, building a fast and scalable vector database with a tunable trade-off between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch.
Czech name
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Czech description
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Project
<a href="/en/project/TD03000295" target="_blank" >TD03000295: Intelligent software for semantic text search</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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