Building High-Dimensional Hypervector Representations for Efficient Entity and Relation Classification in Natural Language Processing

Abstract

The rapid growth of unstructured textual data has led to increased demand for efficient and scalable methods for natural language understanding tasks, such as entity and relation classification. Traditional machine learning models often struggle with the high dimensionality and complexity inherent in natural language data.

Hyperdimensional computing (HDC), inspired by properties of high-dimensional vector spaces and brain-like computation, offers a promising alternative. This article delves into the mathematical underpinnings of HDC, explores its application in encoding and classifying entities and relations within text using the HyperRED dataset, and demonstrates how this approach provides robustness, scalability, and efficiency over conventional methods.

Introduction

In the realm of natural language processing (NLP), understanding the relationships between entities within unstructured text is a fundamental challenge. Tasks such as named entity recognition (NER) and relation extraction (RE) are critical for applications ranging from information retrieval to knowledge graph construction. Traditional approaches, including…