All the World's a Stage: Learning Character Models from Film
All the World's a Stage: Learning Character Models from Film
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世界就是一个舞台：从电影中学习人物模型
DOI：
10.1609/aiide.v7i1.12431
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
M. Walker
中科院分区：
文献类型：
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作者：
Grace I. Lin;M. Walker
许多形式的互动数字娱乐都涉及与虚拟戏剧角色的互动。我们的长期目标是程序化地生成能够自动模仿或融合现有角色风格的角色对话行为。在本文中，我们展示了角色对话中的语言元素如何定义我们的角色扮演游戏《间谍之足》（SpyFeet）中角色的风格。我们利用了来自IMSDb网站的862个电影剧本语料库，这些剧本包含7400个角色、66.4万行对话以及959.9万个单词。我们利用先前用于人格或作者识别的语言反射计数来区分不同的角色类型。通过分类实验，我们表明在20%的基准上，不同类型的角色能够以高达83%的准确率被区分出来。我们讨论了所学习模型的特征，并展示了它们如何被用于模仿特定的电影角色。
Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.