On Quantum Generalizations of Information-Theoretic Measures and their Contribution to Distributional Semantics
This work introduces quantum information theory to distributional semantics, potentially enhancing natural language processing tasks by offering richer models for word interactions.
The paper surveys quantum generalizations of five information-theoretic measures and applies them to distributional semantics, modeling words as density operators to better exploit semantic structure and simulate properties like ambiguity and entailment.
Information-theoretic measures such as relative entropy and correlation are extremely useful when modeling or analyzing the interaction of probabilistic systems. We survey the quantum generalization of 5 such measures and point out some of their commonalities and interpretations. In particular we find the application of information theory to distributional semantics useful. By modeling the distributional meaning of words as density operators rather than vectors, more of their semantic structure may be exploited. Furthermore, properties of and interactions between words such as ambiguity, similarity and entailment can be simulated more richly and intuitively when using methods from quantum information theory.