摘要翻译：
我们将标记云与其他形式的可视化联系起来，包括平面或降维映射，以及Kohonen自组织映射。使用一个修改的标签云可视化，我们将其他信息纳入其中，包括文本序列和最相关的单词。我们对词的相关性的概念不仅仅是词频，而是在数学意义上把一个词看作是它所有成对关系的平均值。我们通过上下文捕获语义，将其视为所有的成对关系。我们的应用领域是filmscript分析。对电影剧本的分析，对于电影来说总是很重要的，在电视背景下，它的重要性正在得到极大的提高。我们在这项工作中的目标是可视化filmscript的语义，以及filmscript之外的任何其他部分结构化的、按时间顺序的文本片段序列。特别是，我们开发了一种创新的方法来刻画情节。
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英文标题：
《Tag Clouds for Displaying Semantics: The Case of Filmscripts》
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作者：
F. Murtagh, A. Ganz, S. McKie, J. Mothe and K. Englmeier
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We relate tag clouds to other forms of visualization, including planar or reduced dimensionality mapping, and Kohonen self-organizing maps. Using a modified tag cloud visualization, we incorporate other information into it, including text sequence and most pertinent words. Our notion of word pertinence goes beyond just word frequency and instead takes a word in a mathematical sense as located at the average of all of its pairwise relationships. We capture semantics through context, taken as all pairwise relationships. Our domain of application is that of filmscript analysis. The analysis of filmscripts, always important for cinema, is experiencing a major gain in importance in the context of television. Our objective in this work is to visualize the semantics of filmscript, and beyond filmscript any other partially structured, time-ordered, sequence of text segments. In particular we develop an innovative approach to plot characterization.
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PDF链接：
https://arxiv.org/pdf/0905.3830