Learning what to read: Focused machine reading

TL;DR

This paper introduces a focused machine reading approach using reinforcement learning to efficiently identify relevant biomedical literature for specific queries, reducing reading costs while improving query answering accuracy.

Contribution

It presents a novel RL-based focused reading algorithm that learns when to explore or exploit literature, outperforming baseline methods in biomedical literature retrieval.

Findings

RL approach reads fewer documents while answering more queries

The method effectively balances exploration and exploitation

Improves efficiency in biomedical literature question answering

Abstract

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today's scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.