Semantic Technology-Assisted Review (STAR) Document analysis and monitoring using random vectors
This addresses the need for efficient document review in fields like legal or research, though it appears incremental as it builds on existing embedding techniques.
The paper tackles the problem of analyzing and monitoring large document collections by using random vectors to create low-dimensional embeddings, enabling fast and accurate computation of semantic similarities for tasks like selection and classification with minimal expert involvement.
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.