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Studies in systems, decision and control

Vector Representation of Words Using Quantum-Like Probabilities

А.В. Платонов, Igor Bessmertny, Julia Koroleva, Lusiena Miroslavskaya, Алаа Шакер

Published Dec 1, 2020• 1 citation
Publisher
DOI
10.1007/978-3-030-65283-8_44

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Real-valued word vectors (RWV) often perform poorly when showing semantic relations such as the ambiguity of the word itself (“spring” can refer to a kind of season or fountain) and the polarity between words (words “love” and “hate” have a high degree of similarity due to their similar distribution). Although dynamic word vectors alleviate these problems, such methods are still lacking interpretability. Inspired by quantum theory, we believe that basic reasons are insufficient modeling ability of RWV and weak abstract ability of language models. To solve the above problems, we propose to use complex-valued word vectors (CWV) for the representation, which can naturally model semantic relations of words. We also design an explicable lexicon-extended quantum probability neural network (LQPNN) based on the theory of quantum measurement to training CWV. Experiments on three datasets of classification demonstrate the effectiveness of our model, which achieves superior performances.
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