A COMPUTATIONAL MODEL FOR PLOT UNITS
A COMPUTATIONAL MODEL FOR PLOT UNITS
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绘图单元的计算模型
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Hal Daumé
中科院分区：
文献类型：
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作者：
Amit Goyal;E. Riloff;Hal Daumé
这项研究重新审视了1980年代开发的情节单位，作为一种概念知识结构，以表示叙事故事中字符之间的影响状态和情感紧张局势，我们提出了一个称为eesop的完全自动化的系统，该系统为叙事文本生成了绘图单位表示。 Aesop执行四个步骤：影响状态识别，角色识别，影响状态投影和链接创建。从现有的词汇资源中缺少：将正极或负极性的动词赋予患者（例如，“饮食”会赋予负极性，因为饮食是不好的，而“喂食”会赋予积极的极性，因为被喂养是好的从Web语料库中自动恢复这些“患者极性动词”（PPV）的技术，并表明PPV改善了状态识别。在一组寓言中的表现，并介绍了一些分析，以阐明当前自然语言处理技术的功能和局限性。
This research revisits plot units, which were developed in the 1980s as a conceptual knowledge structure to represent the affect states of and emotional tensions between characters in narrative stories. We present a fully automated system, called AESOP, that generates plot unit representations for narrative texts. AESOP performs four steps: affect state recognition, character identification, affect state projection, and link creation. We also identify a type of knowledge that seems to be missing from existing lexical resources: verbs that impart positive or negative polarity onto their patients (e.g., “eat” imparts negative polarity because being eaten is bad, whereas “fed” imparts positive polarity because being fed is good). We develop two techniques to automatically harvest these “patient polarity verbs” (PPVs) from a Web corpus, and show that the PPVs improve affect state recognition. Finally, we evaluate AESOP’s performance on a set of fables, and present several analyses to shed light on the capabilities and limitations of current natural language processing technology for plot unit generation.