Interactive Analysis of Word Vector Embeddings
Interactive Analysis of Word Vector Embeddings
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DOI：
10.1111/cgf.13417
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发表时间：
2018-06
影响因子：
2.5
通讯作者：
Florian Heimerl;Michael Gleicher
中科院分区：
文献类型：
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作者：
Florian Heimerl;Michael Gleicher
词向量嵌入是一种新兴的自然语言处理工具。它们已被证明对各种语言处理任务有益。它们的实用性源于在向量空间内编码单词关系的能力。应用范围从自然语言处理系统中的组件到语言和文学研究中的语言分析工具。在许多这些应用程序中，解释嵌入和理解单词之间的编码语法和语义关系是有用的，但具有挑战性。可视化可以帮助这种嵌入的解释。在本文中，我们将研究可视化在使用词向量嵌入中的作用。我们提供了一个文献调查目录的嵌入在广泛的应用程序中使用的任务范围。根据这项调查，我们确定了关键任务及其特征。然后，我们提出了解决这些任务中的许多视觉交互设计。这些设计集成到嵌入的探索和分析环境中。最后，我们为它们提供了示例用例，并讨论了域用户反馈。
Word vector embeddings are an emerging tool for natural language processing. They have proven beneficial for a wide variety of language processing tasks. Their utility stems from the ability to encode word relationships within the vector space. Applications range from components in natural language processing systems to tools for linguistic analysis in the study of language and literature. In many of these applications, interpreting embeddings and understanding the encoded grammatical and semantic relations between words is useful, but challenging. Visualization can aid in such interpretation of embeddings. In this paper, we examine the role for visualization in working with word vector embeddings. We provide a literature survey to catalogue the range of tasks where the embeddings are employed across a broad range of applications. Based on this survey, we identify key tasks and their characteristics. Then, we present visual interactive designs that address many of these tasks. The designs integrate into an exploration and analysis environment for embeddings. Finally, we provide example use cases for them and discuss domain user feedback.