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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| 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 |
| doi_str_mv | 10.48550/arxiv.2011.09351 |
| format | Article |
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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
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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
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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><doi>10.48550/arxiv.2011.09351</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language |
| title | Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models |
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