体验者、刺激或目标：哪些语义角色使机器学习能够推断情感？
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?
摘要：
情感识别主要被公式化为文本分类，其中文本单元被分配给来自预定义库存的情感（例如，恐惧、喜悦、愤怒、厌恶、悲伤、惊讶、信任、期待）。最近，语义角色标注方法已经发展到从文本中提取结构来回答这样的问题：“谁被描述感受到情绪？”（体验者），“是什么导致了这种情绪？“（刺激），它针对哪个实体？“（目标）。虽然它已被证明，联合建模刺激和情绪类别预测是有益的两个子任务，它仍然不清楚这些语义角色，使分类器推断的情绪。是体验者，因为一个人的身份偏向于一种特定的情感（X总是快乐的）？它是一个特定的目标（每个人都喜欢X）还是一个刺激（做X让每个人都伤心）？我们通过在五个可用的数据集上训练情感分类模型来回答这些问题，这些数据集注释了至少一个语义角色，通过以受控的方式掩盖文本中这些角色的填充物，并发现在多个语料库中，刺激和目标携带情感信息，而体验者可能被认为是混淆者。此外，我们分析了通知模型角色的位置是否会改善分类决策。特别是在文学语料库中，我们发现角色信息改善了情感分类。
英文摘要：
Emotion recognition is predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory (e.g., fear, joy, anger, disgust, sadness, surprise, trust, anticipation). More recently, semantic role labeling approaches have been developed to extract structures from the text to answer questions like: "who is described to feel the emotion?" (experiencer), "what causes this emotion?" (stimulus), and at which entity is it directed?" (target). Though it has been shown that jointly modeling stimulus and emotion category prediction is beneficial for both subtasks, it remains unclear which of these semantic roles enables a classifier to infer the emotion. Is it the experiencer, because the identity of a person is biased towards a particular emotion (X is always happy)? Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)? We answer these questions by training emotion classification models on five available datasets annotated with at least one semantic role by masking the fillers of these roles in the text in a controlled manner and find that across multiple corpora, stimuli and targets carry emotion information, while the experiencer might be considered a confounder. Further, we analyze if informing the model about the position of the role improves the classification decision. Particularly on literature corpora we find that the role information improves the emotion classification.
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。
