A Scale-Space Theory for Text

TL;DR

This paper introduces a novel scale-space framework for text that integrates semantic and spatial filters, enabling multi-resolution analysis of documents to improve NLP tasks.

Contribution

It extends scale-space theory from vision to text by combining semantic and spatial filtering for multi-scale text analysis.

Findings

Enables multi-resolution understanding of text documents

Facilitates improved NLP and text analysis tasks

Provides a formal framework for multi-scale text processing

Abstract

Scale-space theory has been established primarily by the computer vision and signal processing communities as a well-founded and promising framework for multi-scale processing of signals (e.g., images). By embedding an original signal into a family of gradually coarsen signals parameterized with a continuous scale parameter, it provides a formal framework to capture the structure of a signal at different scales in a consistent way. In this paper, we present a scale space theory for text by integrating semantic and spatial filters, and demonstrate how natural language documents can be understood, processed and analyzed at multiple resolutions, and how this scale-space representation can be used to facilitate a variety of NLP and text analysis tasks.