Quantum-Like Uncertain Conditionals for Text Analysis

TL;DR

This paper introduces a quantum-inspired probabilistic framework for analyzing natural language text, enabling the creation of topic-specific representations that capture term regularities and user perceptions.

Contribution

It proposes a novel logical-probabilistic approach based on Uncertain Conditionals and quantum measurement concepts for text analysis.

Findings

Detects regularities in term usage across texts in different languages

Generates topic-specific text representations aligned with user perceptions

Demonstrates the approach with bilingual text examples

Abstract

Simple representations of documents based on the occurrences of terms are ubiquitous in areas like Information Retrieval, and also frequent in Natural Language Processing. In this work we propose a logical-probabilistic approach to the analysis of natural language text based in the concept of Uncertain Conditional, on top of a formulation of lexical measurements inspired in the theoretical concept of ideal quantum measurements. The proposed concept can be used for generating topic-specific representations of text, aiming to match in a simple way the perception of a user with a pre-established idea of what the usage of terms in the text should be. A simple example is developed with two versions of a text in two languages, showing how regularities in the use of terms are detected and easily represented.