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Recognition of Affect, Judgment, and Appreciation in Text

机译:文本中对情感,判断和欣赏的识别

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The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the finegrained level (14 labels) was 62%, on the middle level (7 labels) – 71%, and on the top level (3 labels) – 88%.
机译:我们研究中要解决的主要任务是使用细粒度的姿态标签对文本进行分类。已开发的@AM系统依赖于构图性原则和一种基于针对语义上不同的动词类制定的规则的新颖方法。我们对1000条描述个人经历的句子进行的方法评估显示出令人鼓舞的结果:细粒度级别(14个标记)的平均准确度为62%,中等级别(7个标记)-71%,以及最高级别( 3个标签)– 88%。

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