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arXiv · 1606.07660

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

Christina Lioma·Birger Larsen·Casper Petersen·Jakob Grue Simonsen
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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.

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Christina Lioma, Birger Larsen, Casper Petersen, Jakob Grue Simonsen. 2016-06-24. Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it). https://arxiv.org/abs/1606.07660

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