Quantum-Inspired Non-Homologous Representation Constraint Mechanism for Long-Tail Senses of Word Sense Disambiguation

Abstract

Word Sense Disambiguation (WSD) aims to determine the meaning of target words according to the given context. The recognition of high-frequency senses has reached expectations, and the current research focus is mainly on low-frequency senses, namely Long-tail Senses (LTSs). One of the challenges in long-tail WSD is to obtain clear and distinguishable definition representations based on limited word sense definitions. Researchers try to mine word sense definition information from data from different sources to enhance the representations. Inspired by quantum theory, this paper provides a constraint mechanism for representations under non-homogeneous data to leverage the geometric relationship in its Hilbert space to constrain the value range of parameters, thereby alleviating the dependence on big data and improving the accuracy of representations. We theoretically analyze the feasibility of the constraint mechanism, and verify the WSD system based on this mechanism on the standard evaluation framework, constructed LTS datasets and cross-lingual datasets. Experimental results demonstrate the effectiveness of the scheme and achieve competitive performance.