Meaning-focused and Quantum-inspired Information Retrieval

TL;DR

This paper introduces a quantum-inspired framework for information retrieval that models concepts as entities of meaning and reconstructs their full states from document traces, offering advantages over traditional methods like LSA.

Contribution

It proposes a novel quantum meaning-based approach for structured query retrieval, integrating quantum structures into IR and NLP based on entities of meaning and their traces.

Findings

The framework effectively reconstructs full states of concepts from document traces.

It demonstrates advantages over traditional IR methods such as LSA.

Concrete examples illustrate the benefits of the quantum-inspired approach.

Abstract

In recent years, quantum-based methods have promisingly integrated the traditional procedures in information retrieval (IR) and natural language processing (NLP). Inspired by our research on the identification and application of quantum structures in cognition, more specifically our work on the representation of concepts and their combinations, we put forward a 'quantum meaning based' framework for structured query retrieval in text corpora and standardized testing corpora. This scheme for IR rests on considering as basic notions, (i) 'entities of meaning', e.g., concepts and their combinations and (ii) traces of such entities of meaning, which is how documents are considered in this approach. The meaning content of these 'entities of meaning' is reconstructed by solving an 'inverse problem' in the quantum formalism, consisting of reconstructing the full states of the entities of meaning from their collapsed states identified as traces in relevant documents. The advantages with respect to traditional approaches, such as Latent Semantic Analysis (LSA), are discussed by means of concrete examples.