论文标题
哨兵：使用单词共同出现和情感分析来解释视觉分析中模糊的意图修饰符
Sentifiers: Interpreting Vague Intent Modifiers in Visual Analysis using Word Co-occurrence and Sentiment Analysis
论文作者
论文摘要
与数据可视化工具的自然语言互动通常涉及使用模糊的主观修饰符，例如“向我展示执行的扇区”和“在哪里可以买房的好社区？”。这些工具通常很难解释这些修饰符，因为它们的含义缺乏清晰的语义，部分是由上下文和个人用户偏好定义的。本文提出了一个称为\系统的系统，该系统迈出了更好地理解这些模糊谓词的第一步。该算法采用单词共存在和情感分析来确定哪些数据属性和过滤器范围与模糊的谓词相关。该算法的出处结果是作为可以修复和完善的交互式文本的用户。我们对哨兵系统进行定性评估，该评估指示界面的有用性，以及通过自然语言在视觉分析任务中更好地支持主观话语的机会。
Natural language interaction with data visualization tools often involves the use of vague subjective modifiers in utterances such as "show me the sectors that are performing" and "where is a good neighborhood to buy a house?." Interpreting these modifiers is often difficult for these tools because their meanings lack clear semantics and are in part defined by context and personal user preferences. This paper presents a system called \system that makes a first step in better understanding these vague predicates. The algorithm employs word co-occurrence and sentiment analysis to determine which data attributes and filters ranges to associate with the vague predicates. The provenance results from the algorithm are exposed to the user as interactive text that can be repaired and refined. We conduct a qualitative evaluation of the Sentifiers system that indicates the usefulness of the interface as well as opportunities for better supporting subjective utterances in visual analysis tasks through natural language.
