Deep learning relevance: creating relevant information (as opposed to retrieving it)

TY - CONF

T1 - Deep learning relevance

T2 - SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)

AU - Lioma, Christina

AU - Larsen, Birger

AU - Petersen, Casper

AU - Simonsen, Jakob Grue

N1 - Conference code: 1

PY - 2016

Y1 - 2016

N2 - 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 singledocument? 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.

AB - 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 singledocument? 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.

M3 - Paper

Y2 - 21 July 2016 through 21 July 2016

ER -