Další formáty:
BibTeX
LaTeX
RIS
@inproceedings{1392125, author = {Růžička, Michal and Novotný, Vít and Sojka, Petr and Pomikálek, Jan and Řehůřek, Radim}, address = {Vienna, Austria}, booktitle = {CEUR Workshop Proceedings, Vol. 1923}, keywords = {vector space modelling; semantic vectors encodings; inverted-index; systems performance; document representations; Latent Semantic Analysis; doc2vec; GloVe; Elasticsearch; evaluation; performance optimization}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Vienna, Austria}, pages = {1-12}, publisher = {Neuveden}, title = {Flexible Similarity Search of Semantic Vectors Using Fulltext Search Engines}, url = {https://usc-isi-i2.github.io/ISWC17workshop/}, year = {2017} }
TY - JOUR ID - 1392125 AU - Růžička, Michal - Novotný, Vít - Sojka, Petr - Pomikálek, Jan - Řehůřek, Radim PY - 2017 TI - Flexible Similarity Search of Semantic Vectors Using Fulltext Search Engines PB - Neuveden CY - Vienna, Austria KW - vector space modelling KW - semantic vectors encodings KW - inverted-index KW - systems performance KW - document representations KW - Latent Semantic Analysis KW - doc2vec KW - GloVe KW - Elasticsearch KW - evaluation KW - performance optimization UR - https://usc-isi-i2.github.io/ISWC17workshop/ N2 - 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. ER -
RŮŽIČKA, Michal, Vít NOVOTNÝ, Petr SOJKA, Jan POMIKÁLEK a Radim ŘEHŮŘEK. Flexible Similarity Search of Semantic Vectors Using Fulltext Search Engines. Online. In \textit{CEUR Workshop Proceedings, Vol. 1923}. Vienna, Austria: Neuveden, 2017, s.~1-12. ISSN~1613-0073.
|