论文标题
代币序列标记与英语情感刺激检测的子句分类
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection
论文作者
论文摘要
情绪刺激检测是在文本描述中找到情感原因的任务，类似于目标或方面检测以进行情感分析。以前的工作以三种方式（1）将文本分类作为预定义的可能的刺激的清单（“刺激类别A或B？”），（2）作为令牌的序列标记（“哪个令牌描述刺激？”），并且（3）作为条款分类（“该条款刺激刺激）？”）。到目前为止，已经广泛评估了（3）的设置（3），并且（2）在英语上进行了评估，但没有进行比较。因此，我们旨在回答子句分类或序列标签是否更适合于英语中的情感刺激检测。为此，我们提出了一个集成的框架，使我们能够评估两种不同的方法，即以普通话中最新方法启发的实施模型，并对来自不同域中的四个英语数据集进行测试。我们的结果表明，在基于子句和基于序列的评估中，在四个数据集中的三个数据集中，序列标记在四个数据集中都出色。从句分类更好的唯一情况是一个数据集，该数据集具有较高的子句注释密度。我们的错误分析进一步定量和定性地证实了条款不是英语中适当的刺激单元。
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.
