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Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models

2020-11-16 07:20:02
Chaofan Tu, Ruibin Bai, Zheng Lu, Uwe Aickelin, Peiming Ge, Jianshuang Zhao
arXiv_AI
         
Abstract

In this paper, we propose a rule-based engine composed of high quality and interpretable regular expressions for medical text classification. The regular expressions are auto generated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present high quality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable black boxes to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labor-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions

Abstract (translated)

URL

https://arxiv.org/abs/2011.09351

PDF

https://arxiv.org/pdf/2011.09351.pdf

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