An Automatic Dictionary Extraction and Annotation Method Using Simulated Annealing for Detecting Human Values
An Automatic Dictionary Extraction and Annotation Method Using Simulated Annealing for Detecting Human Values
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一种利用模拟退火检测人类价值观的自动词典提取和标注方法
DOI：
10.1109/iiai-aai.2015.268
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
and Emi Ishita
中科院分区：
文献类型：
--
作者：
Yasuhiro Takayama;Yoichi Tomiura;Kenneth Fleischmann;An-Shou Cheng;Doug Oard;and Emi Ishita
本文研究了一种识别与自由或创新等一种或多种人类价值相关联的单词单字和单词大公羊的方法。其关键思想是确定地将值与单词选择相关联，从而允许使用词典查找来分配句子所反映的值。这种方法的平均效果几乎与现有的最准确的方法一样好，接近第二个人类注释器可以达到的最佳结果，但新方法的主要贡献是，系统分类决策的基础更容易被社会科学家解释。新方法基于蒙特卡罗算法和模拟退火法，以有效地探索将人类价值最优分配给单星和大公羊的空间。结果是在一份带有注释的测试集上报告的，这些证词来自公开听证会上关于网络中立主题的证人准备的证词。结果包括与以前报道的方法的准确性比较。
This paper studies a method for identifying word unigrams and word big rams 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 big rams. 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.