Token序列标记与小句分类在英语情绪刺激检测中的应用
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection
摘要：
情感刺激检测是在文本描述中找到情感原因的任务，类似于用于情感分析的目标或方面检测。以前的工作接近这三种方式，即（1）作为文本分类到预定义的可能的刺激（“是刺激类别A或B？（2）标记的序列标记（“哪些标记描述刺激？（3）作为小句分类（“本小句是否包含情感刺激？").到目前为止，设置（3）已被广泛评估的普通话和（2）的英语，但没有进行比较。因此，我们的目标是回答是否小句分类或序列标记更适合于在英语中的情感刺激检测。为了实现这一目标，我们提出了一个综合框架，使我们能够评估两种不同的方法，在普通话中实现受最先进方法启发的模型，并在来自不同领域的四个英语数据集上进行测试。我们的研究结果表明，序列标记是优于三出四个数据集，在基于子句和基于序列的评估。子句分类执行得更好的唯一情况是具有高密度子句注释的数据集。我们的错误分析进一步从定量和定性上证实了小句不是英语中合适的刺激单位。
英文摘要：
Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli ("Is the stimulus category A or B?"), (2) as sequence labeling of tokens ("Which tokens describe the stimulus?"), and (3) as clause classification ("Does this clause contain the emotion stimulus?"). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we aim to answer whether clause classification or sequence labeling is better suited for emotion stimulus detection in English. To accomplish that, we propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that sequence labeling is superior on three out of four datasets, in both clause-based and sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
