Text Classification with Born's RuleDownload PDF

Published: 31 Oct 2022, Last Modified: 05 Oct 2022NeurIPS 2022 AcceptReaders: Everyone
Keywords: text classification, quantum physics, quantum-inspired machine learning, explainable classification
Abstract: This paper presents a text classification algorithm inspired by the notion of superposition of states in quantum physics. By regarding text as a superposition of words, we derive the wave function of a document and we compute the transition probability of the document to a target class according to Born's rule. Two complementary implementations are presented. In the first one, wave functions are calculated explicitly. The second implementation embeds the classifier in a neural network architecture. Through analysis of three benchmark datasets, we illustrate several aspects of the proposed method, such as classification performance, explainability, and computational efficiency. These ideas are also applicable to non-textual data.
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