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An Automatic Dictionary Extraction and Annotation Method Using Simulated Annealing for Detecting Human Values

机译:使用模拟退火检测人类价值的自动词典提取和注释方法

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This paper studies a method for identifying word unigrams and word bigrams that are associated with one or more human values such as freedom or innovation. The key idea is to deterministically associate values with word choices, thus permitting values reflected by sentences to be assigned using dictionary lookup. This approach works nearly as well on average as the most accurate existing methods, and at close to the best results that can be achieved by a second human annotator, but the principal contribution of the new method is that the basis for the system's classification decisions are more easily interpreted by social scientists. The new method is based using a Monte Carlo algorithm with simulated annealing to efficiently explore the space for optimal assignments of human values to unigrams and bigrams. Results are reported on an annotated test collection of prepared statements from witnesses at public hearings on the topic of net neutrality. The results include accuracy comparisons with the previously reported approach.
机译:本文研究了一种识别与一个或多个人类价值相关的单词Unigrams和Word Bigrams的方法,例如自由或创新。关键的想法是使用单词选择来确定与单词选择相关的值,从而允许使用句子反映的值来使用字典查找分配。这种方法与最准确的现有方法平均以及最准确的现有方法工作,并且在第二人体注册商可以实现的最佳结果,但新方法的主要贡献是系统分类决策的基础是更容易被社会科学家解释。新方法是基于Monte Carlo算法,模拟退火用于有效地探索人类价值的最佳分配到Unigrams和Bigrams的空间。结果报告了关于在网络中立议题的公开听证会上的证人的准备陈述的注释测试收集。结果包括与先前报告的方法的准确性比较。

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