Understanding Negations in Information Processing: Learning from Replicating Human Behavior
This addresses the problem of improving information processing in systems like recommender systems by handling negations more effectively, though it appears incremental as it builds on existing reinforcement learning approaches for a specific bottleneck.
The paper tackles the challenge of correctly inferring meaning from textual data, specifically negations that invert interpretation, by proposing a reinforcement learning strategy to replicate human perception of negations based on exogenous responses like user ratings. The result is a method that eliminates the need for expensive manual labeling and allows for statistical inferences about human processing of negations.
Information systems experience an ever-growing volume of unstructured data, particularly in the form of textual materials. This represents a rich source of information from which one can create value for people, organizations and businesses. For instance, recommender systems can benefit from automatically understanding preferences based on user reviews or social media. However, it is difficult for computer programs to correctly infer meaning from narrative content. One major challenge is negations that invert the interpretation of words and sentences. As a remedy, this paper proposes a novel learning strategy to detect negations: we apply reinforcement learning to find a policy that replicates the human perception of negations based on an exogenous response, such as a user rating for reviews. Our method yields several benefits, as it eliminates the former need for expensive and subjective manual labeling in an intermediate stage. Moreover, the inferred policy can be used to derive statistical inferences and implications regarding how humans process and act on negations.