Ordered Semantically Diverse Sampling for Textual Data

2025-03-12

AI Summary

This paper addresses the problem of **ordered diversity sampling** from text data—selecting a compact yet semantically broad, information-dense subset that preserves natural sequential structure. We introduce the first formal **ordered diversity metric** tailored for ranked lists and propose an efficient sampling framework integrating text embeddings with principal component analysis (PCA) to enable scalable optimization. Evaluated on a reconstructed text classification benchmark, our method outperforms state-of-the-art approaches by 6%–61% in reconstruction fidelity while substantially reducing computational overhead. Ablation studies confirm the critical roles of both the ordered diversity metric and PCA-driven sampling. Our core contributions are: (1) the first rigorous, formal definition of ordered diversity; and (2) an end-to-end sampling paradigm that jointly optimizes semantic coverage, sequence fidelity, and computational efficiency.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

The goal of diversity sampling is to select a representative subset of data in a way that maximizes information contained in the subset while keeping its cardinality small. We introduce the ordered diverse sampling problem based on a new metric that measures the diversity in an ordered list of samples. We present a novel approach for generating ordered diverse samples for textual data that uses principal components on the embedding vectors. The proposed approach is simple and compared with existing approaches using the new metric. We transform standard text classification benchmarks into benchmarks for ordered diverse sampling. Our empirical evaluation shows that prevailing approaches perform 6% to 61% worse than our method while also being more time inefficient. Ablation studies show how the parts of the new approach contribute to the overall metrics.

Problem

• Maximize information in small data subsets
• Measure diversity in ordered text samples
• Improve efficiency and accuracy in text sampling

Innovation

• Uses principal components on embedding vectors
• Introduces ordered diverse sampling problem
• Transforms text benchmarks for ordered sampling