Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)
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.
Cite
@article{arxiv.1606.07660,
title = {Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)},
author = {Christina Lioma and Birger Larsen and Casper Petersen and Jakob Grue Simonsen},
journal= {arXiv preprint arXiv:1606.07660},
year = {2016}
}
Comments
Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21, 2016, Pisa, Italy