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

Publikation: KonferencebidragPaperForskningfagfællebedømt

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.
Antal sider6
StatusUdgivet - 2016
BegivenhedSIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR) - Pisa, Italien
Varighed: 21 jul. 201621 jul. 2016
Konferencens nummer: 1


KonferenceSIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)


ID: 171795008