发表机构
University of Stuttgart; Bocconi University（斯图加特大学; 博科尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对NLP领域视角相关概念关系不明确的问题，通过定义区分属性构建概念层次结构，助力研究人员选择匹配研究目标的视角操作化方式。
AI 中文摘要
同一事件可根据作者或说话者的经历、背景和信念从不同视角进行报道。自然语言处理（NLP）的多个领域都涉及视角问题，涵盖从文本分析到算法优化等方向。人们已使用多种可操作概念（如立场（stance）、情感（sentiment）、框架（frame）和论证（argument））来捕捉文本中的视角，但这些概念之间的精确关系仍不明确。可以说，对这些概念进行更深入的理论理解将推动更有效的视角相关研究。本文通过综述NLP领域的视角空间并定义一组用于区分视角相关概念的属性，来解决这一空白。我们的分析使我们提出一个层次结构，将这些概念沿单一轴线线性组织。最后，我们展示这一原则性概念层次结构如何帮助研究人员在该领域中定位方向，并选择与其特定研究目标一致的视角操作化方式。
英文摘要
The same event can be reported from different perspectives depending on the experiences, background, and beliefs of the writer or speaker. A variety of NLP areas engage with perspectives, spanning from text analysis to algorithm optimization. A wide range of operative concepts (such as stances, sentiment, frames, and arguments) has been used to capture perspectives in texts, however the precise relationships among those concepts remain unclear. Arguably, a deeper theoretical understanding of these concepts would empower more effective research on perspectives. In this paper, we address this gap by reviewing the space of perspectives in NLP and defining a set of properties that help distinguishing perspective-related concepts. Our analysis leads us to posit a hierarchy which organizes these concepts linearly along a single axis. Finally, we show how this principled conceptual hierarchy can help researchers navigate the field and select operationalizations of perspective that align with their specific research objectives.
CommentsUnder review for TACL (editor decision: b)
