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

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 ex...

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Veröffentlicht in:	arXiv.org 2020-11
Hauptverfasser:	Tu, Chaofan, Bai, Ruibin, Lu, Zheng, Aickelin, Uwe, Ge, Peiming, Zhao, Jianshuang
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Sprache:	eng
Schlagworte:	Artificial neural networks Classification Heuristic methods Machine learning Natural language processing Simulated annealing
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creator	Tu, Chaofan Bai, Ruibin Lu, Zheng Aickelin, Uwe Ge, Peiming Zhao, Jianshuang
description	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
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subjects	Artificial neural networks Classification Heuristic methods Machine learning Natural language processing Simulated annealing
title	Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models
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