Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)

Christina Lioma, Birger Larsen, Casper Petersen, Jakob Grue Simonsen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstract

What if Information Retrieval (IR) systems did not just retrieve relevant information that is stored in their indices, but could also "understand" it and synthesise it into a single document? We present a preliminary study that makes a first step towards answering this question. Given a query, we train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all.
OriginalsprogEngelsk
TitelNeu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Italy
Publikationsdato2016
StatusUdgivet - 2016
BegivenhedNeu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Italy - Pisa, Italien
Varighed: 21 jul. 201621 jul. 2016

Workshop

WorkshopNeu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Italy
Land/OmrådeItalien
ByPisa
Periode21/07/201621/07/2016

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