Semantic Technology-Assisted Review (STAR) Document analysis and monitoring using random vectors

TL;DR

This paper introduces STAR, a semantic technology that uses random vectors to embed words and documents into a low-dimensional space, enabling fast, accurate analysis and monitoring of large document collections with minimal expert input.

Contribution

The paper presents a novel application of random vector embeddings for semantic analysis, improving efficiency and accuracy in document review and monitoring tasks.

Findings

Fast computation of semantic similarities

High-quality document classification and summarization

Minimal expert involvement required

Abstract

The review and analysis of large collections of documents and the periodic monitoring of new additions thereto has greatly benefited from new developments in computer software. This paper demonstrates how using random vectors to construct a low-dimensional Euclidean space embedding words and documents enables fast and accurate computation of semantic similarities between them. With this technique of Semantic Technology-Assisted Review (STAR), documents can be selected, compared, classified, summarized and evaluated very quickly with minimal expert involvement and high-quality results.