隐性知识获取技术手册中歧义句的机器学习

Naoto Kai, Kota Sakasegawa, Tsunenori Mine, S. Hirokawa
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引用次数: 2

摘要

本研究的目的是通过判断歧义句来挖掘隐性知识。本研究以铁路车辆的检查与维修为研究对象。为了验证歧义句包含隐性知识的假设,我们比较了人类和机器学习的挖掘结果。结果表明,通过机器判断可以对歧义句进行识别,且准确率较高。从数据分析中得出的最惊人的结论是,歧义句不仅可以通过形容词和副词来识别,还可以通过名词、后置助词或连词来识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning of Ambiguous Sentences in Technical Manual for Tacit Knowledge Acquisition
The objective of this study is to judge ambiguous sentences for mining tacit knowledge. This study was conducted with inspections and maintenance of railway rolling stock as the subject. To test the hypothesis that ambiguous sentences include tacit knowledge, we compared result of mining by human and by machine learning. We obtained the results that we can recognize the ambiguous sentences by machinery judgement with high accuracy. The most striking observation to emerge from this data analysis was that ambiguous sentences can identified not only by adjectives and adverbs but also nouns, post positional particle, or conjunctions.
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