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        <title>On Efficient Retrieval of Top Similarity Vectors</title>
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    <abstract>Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks. In this paper, we demonstrate an efficient method for searching vectors via a typical non-metric matching function: inner product. Our method, which constructs an approximate Inner Product Delaunay Graph (IPDG) for top-1 Maximum Inner Product Search (MIPS), transforms retrieving the most suitable latent vectors into a graph search problem with great benefits of efficiency. Experiments on data representations learned for different machine learning tasks verify the outperforming effectiveness and efficiency of the proposed IPDG.</abstract>
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%0 Conference Proceedings
%T On Efficient Retrieval of Top Similarity Vectors
%A Tan, Shulong
%A Zhou, Zhixin
%A Xu, Zhaozhuo
%A Li, Ping
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F tan-etal-2019-efficient
%X Retrieval of relevant vectors produced by representation learning critically influences the efficiency in natural language processing (NLP) tasks. In this paper, we demonstrate an efficient method for searching vectors via a typical non-metric matching function: inner product. Our method, which constructs an approximate Inner Product Delaunay Graph (IPDG) for top-1 Maximum Inner Product Search (MIPS), transforms retrieving the most suitable latent vectors into a graph search problem with great benefits of efficiency. Experiments on data representations learned for different machine learning tasks verify the outperforming effectiveness and efficiency of the proposed IPDG.
%R 10.18653/v1/D19-1527
%U https://aclanthology.org/D19-1527
%U https://doi.org/10.18653/v1/D19-1527
%P 5236-5246

Markdown (Informal)

[On Efficient Retrieval of Top Similarity Vectors](https://aclanthology.org/D19-1527) (Tan et al., EMNLP-IJCNLP 2019)

• On Efficient Retrieval of Top Similarity Vectors (Tan et al., EMNLP-IJCNLP 2019)

ACL

• Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, and Ping Li. 2019. On Efficient Retrieval of Top Similarity Vectors. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5236–5246, Hong Kong, China. Association for Computational Linguistics.