An anatomy for neural search engines

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dc.creatorThiago Akio Nakamura
dc.creatorPedro H. Calais
dc.creatorDavi de Castro Reis
dc.creatorAndré Paim Lemos
dc.date.accessioned2025-05-15T13:27:25Z
dc.date.accessioned2025-09-08T23:25:24Z
dc.date.available2025-05-15T13:27:25Z
dc.date.issued2019
dc.identifier.doi10.1016/j.ins.2018.12.041
dc.identifier.issn00200255
dc.identifier.urihttps://hdl.handle.net/1843/82291
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofInformation Sciences
dc.rightsAcesso Aberto
dc.subjectRedes neurais (Computação)
dc.subjectCiência da Computação
dc.subject.otherInformation retrieval
dc.subject.otherMachine learning
dc.subject.otherDeep learning
dc.subject.otherNeural IR
dc.subject.otherSearch engine
dc.titleAn anatomy for neural search engines
dc.typeArtigo de periódico
local.citation.epage353
local.citation.spage339
local.citation.volume480
local.description.resumoIn this work, we explore the application of modern deep learning techniques to build a neural model centric search engine. We conduct an in-depth discussion under several quantitative and qualitative criteria, comparing the trade-offs of adopting the proposed neural architecture against the successful and mature traditional information retrieval techniques. We show that a full neural architecture, which employs neural models both in the retrieval and ranking phases, offers good scalability, predictability and evolution properties, and discuss under which conditions one can achieve state-of-the-art results. We conclude that deep learning centric systems still require significant more effort to implement and deploy and demand more computational resources, but this work, together with several others in the research community, sheds a light into that path.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0020025518309952

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