Spacewalker让专家快速探索和标注海量非结构化文本数据。
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
- 通过低维空间可视化与交互式遍历，实现多模态数据探索。
- 相比传统方法，显著降低数据验证与标注的时间与人力成本。
- 适合医疗、金融等需深度文本分析的领域专家使用。
在医疗、金融和制造等行业中，非结构化文本数据的分析对决策带来巨大挑战。从大规模语料库中发现模式并理解其语义影响至关重要，但通常依赖领域专家或耗时的人工审查。为此，本文提出Spacewalker，一个用于跨模态数据分析、探索与标注的交互式工具。用户可通过提取数据表示，在低维空间中进行可视化，并支持探索性浏览或针对感兴趣区域的查询。我们通过大量实验与标注研究评估了该工具在提升数据完整性验证与标注效率方面的表现，结果表明其相较传统方法显著减少时间与精力投入。相关代码已开源，地址为：https://github.com/code-lukas/Spacewalker。
原文摘要 · Abstract (English)
In industries such as healthcare, finance, and manufacturing, analysis of unstructured textual data presents significant challenges for analysis and decision making. Uncovering patterns within large-scale corpora and understanding their semantic impact is critical, but depends on domain experts or resource-intensive manual reviews. In response, we introduce Spacewalker in this system demonstration paper, an interactive tool designed to analyze, explore, and annotate data across multiple modalities. It allows users to extract data representations, visualize them in low-dimensional spaces and traverse large datasets either exploratory or by querying regions of interest. We evaluated Spacewalker through extensive experiments and annotation studies, assessing its efficacy in improving data integrity verification and annotation. We show that Spacewalker reduces time and effort compared to traditional methods. The code of this work is open-source and can be found at: https://github.com/code-lukas/Spacewalker
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