Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data

2024-09-25

arXiv.org

AI Summary

To address the challenges of discovering latent patterns in unstructured text from domains such as healthcare and finance, high manual annotation costs, and strong reliance on domain experts, this paper proposes a navigable representation space paradigm and develops an interactive multimodal text exploration and annotation system. Methodologically, it integrates pretrained language model embeddings, t-SNE/UMAP-based low-dimensional visualization, and approximate nearest-neighbor indexing to accelerate region retrieval, enabling semantic-query-driven exploratory navigation. The key contribution lies in modeling the representation space as a navigable structure, thereby unifying exploratory analysis and targeted querying while substantially reducing dependence on manual review. Experiments demonstrate a 3.2× improvement in annotation efficiency and a 67% reduction in data completeness verification time. The open-source implementation has been widely adopted by the research community.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

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

Problem

• Unstructured Text Analysis
• Information Extraction
• Domain Expertise Dependence

Innovation

• Spacewalker
• Efficiency Enhancement
• Open-source Tool