Semantic information retrieval realized by neural network technology and distributed representation

Abstract

Information retrieval technology has evolved from simple keyword matching to semantic understanding-based search. This paper provides an overview of technological evolution from the TF-IDF-based vector space model in the 1970s to modern neural network technology. This article focuses particularly on the breakthrough brought by distributed word representations through Word2Vec, which enabled numerical handling of semantic relationships between words. This innovation has made it possible to properly handle synonyms and related terms, realizing more flexible search capabilities. Furthermore, with the emergence of large language models, search systems capable of deeper contextual understanding have become possible.