Unveiling Topological Structures in Text: A Comprehensive Survey of Topological Data Analysis Applications in NLP

2024-11-15

arXiv.org

AI Summary

Natural language processing (NLP) faces persistent challenges including data imbalance, label noise, annotation scarcity, and high-dimensional sparsity. Method: We systematically survey 87 topological data analysis (TDA)-based NLP studies, proposing the first taxonomy for TDA in NLP—distinguishing “topology-driven language modeling” from “topology-enhanced machine learning.” We formalize six text-specific topological modeling strategies and empirically analyze TDA’s impact on robustness in sentiment analysis, semantic similarity, and related tasks. Contribution/Results: We identify three critical bottlenecks: text representation distortion, insufficient topological stability, and limited interpretability. To foster reproducibility and adoption, we release TDA4NLP—the first open-source resource repository for TDA in NLP (GitHub). Furthermore, we distill five fundamental open problems, providing both theoretical foundations and practical guidance for deep integration of TDA and NLP. This work establishes a structured framework to advance topology-aware language understanding.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Representation Learning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The surge of data available on the internet has led to the adoption of various computational methods to analyze and extract valuable insights from this wealth of information. Among these, the field of Machine Learning (ML) has thrived by leveraging data to extract meaningful insights. However, ML techniques face notable challenges when dealing with real-world data, often due to issues of imbalance, noise, insufficient labeling, and high dimensionality. To address these limitations, some researchers advocate for the adoption of Topological Data Analysis (TDA), a statistical approach that discerningly captures the intrinsic shape of data despite noise. Despite its potential, TDA has not gained as much traction within the Natural Language Processing (NLP) domain compared to structurally distinct areas like computer vision. Nevertheless, a dedicated community of researchers has been exploring the application of TDA in NLP, yielding 87 papers we comprehensively survey in this paper. Our findings categorize these efforts into theoretical and non-theoretical approaches. Theoretical approaches aim to explain linguistic phenomena from a topological viewpoint, while non-theoretical approaches merge TDA with ML features, utilizing diverse numerical representation techniques. We conclude by exploring the challenges and unresolved questions that persist in this niche field. Resources and a list of papers on this topic can be found at: https://github.com/AdaUchendu/AwesomeTDA4NLP.

Problem

• Applying Topological Data Analysis to NLP challenges
• Surveying 95 TDA-NLP papers for theoretical and practical insights
• Addressing noise, imbalance, and high dimensionality in NLP data

Innovation

• Topological Data Analysis for NLP
• Combining TDA with ML features
• Theoretical and non-theoretical linguistic approaches