# Understanding Negations in Information Processing: Learning from Replicating Human Behavior

## TL;DR

This paper introduces a reinforcement learning approach to detect negations in textual data by mimicking human perception, reducing manual labeling and providing insights into human processing of negations.

## Contribution

It presents a novel reinforcement learning method to identify negations that replicates human perception without manual labeling, enhancing understanding of human language processing.

## Key findings

- Eliminates need for manual labeling in negation detection
- Replicates human perception of negations using reinforcement learning
- Provides insights into human processing of negations

## Abstract

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.