基于词向量模型和模拟退火的正则表达式在医学文本分类中的应用
Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models
摘要：
在本文中，我们提出了一个基于规则的引擎组成的高质量和可解释的正则表达式的医学文本分类。正则表达式由构造性探试法自动生成，并使用基于池的模拟退火（PSA）方法进行优化。尽管现有的深度神经网络（DNN）方法在大多数自然语言处理（NLP）应用中表现出高质量的性能，但这些解决方案被认为是人类无法解释的黑箱。因此，当需要可解释的解决方案时，通常会引入基于规则的方法，特别是在医学领域。然而，对于大型数据集，正则表达式的构造可能是极其劳动密集型的。本研究旨在减少人工工作，同时保持高质量的解决方案
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
