正则语言推断综述  

A Survey on Regular Language Inference

作　　者：高俊涛[1] 王梅[1] 徐光会 刘聪 GAO Jun-tao;WANG Mei;XU Guang-hui;LIU Cong(School of Computer and Information Technology,Northeast Petroleum University,Daqing,Heilongjiang 163318,China;School of Computer Science and Technology,Shandong University of Technology,Zibo,Shandong 255000,China)

机构地区：[1]东北石油大学计算机与信息技术学院,黑龙江大庆163318 [2]山东理工大学计算机科学与技术学院,山东淄博255000

出　　处：《电子学报》2021年第12期2479-2489,共11页Acta Electronica Sinica

基　　金：国家自然科学基金(No.51774090,No.61902222);山东省泰山学者工程专项基金资助项目(No.tsqn201909109);大庆市指导性科技计划项目(No.zd-2019-22)。

摘　　要：正则语言推断研究从语言的有限信息出发,通过归纳和推理得出正则语言模型.该技术在信息抽取、软件工程、模式识别等领域应用广泛.本文首先阐明了语言的可学习性概念和推断结果的评价准则.然后从推理策略、数据结构、算法复杂性等方面,对被动、主动和基于神经网络的学习算法进行分类归纳与对比,梳理各流派的技术发展脉络.接着分析推断产生的三种泛化效应.最后指出当前研究中不足,对未来研究方向进行展望.The regular language inference is devoted to the study of how to derive the definition of the language from the limited information.Regular language inference is popular in information extraction,software engineering,and schema learning,etc.The learnability models of regular languages and evaluation criteria are clarified.The passive,active,and neu⁃ral network⁃based learning algorithms are respectively surveyed and compared from the aspects of reasoning strategies,data structures,algorithm complexity,etc,and the development trend is pointed out.Three kinds of generalization effects are an⁃alyzed.This paper points out the defects of the current researches and looks forward to future research directions.

关 键 词：正则语言 归纳学习 正则推断 自动机学习 正则表达式学习 循环神经网络 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]