The Effect of Semantic Interaction on Foraging in Text Analysis
The Effect of Semantic Interaction on Foraging in Text Analysis
复制标题
文本分析中语义交互对觅食的影响
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Chris North
中科院分区：
文献类型：
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作者：
John E. Wenskovitch;Lauren Bradel;Michelle Dowling;L. House;Chris North
完成文本分析任务是一个连续的意义构建循环，即搜寻信息并逐步将其合成为假设。过去的研究表明，使用空间的优势，作为一种手段，综合信息，通过外部化的假设和创建空间模式。然而，随着分析师可用的文档数量的增加，空间化整个数据集变得令人望而却步，特别是当只有一小部分与手头的任务相关时。StarSPIRE是一个可视化分析工具，旨在探索文档集合，利用用户的语义交互来引导（1）辅助文档布局的合成模型，以及（2）自动检索新相关信息的觅食模型。与传统的关键词搜索觅食（KSF）相比，“语义交互觅食”（SIF）是用户综合行为的结果。为了量化语义交互觅食的价值，我们使用StarSPIRE来评估其在智能分析意义构建任务中的实用性。语义交互觅食占发现的有用文档的26%，与仅使用关键字搜索相比，它还导致了用户合成交互的增加和意义构建任务性能的提高。
Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users’ semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), “semantic interaction foraging” (SIF) occurs as a result of the user’s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.