An Algorithm of Semantic Similarity Between Words Based on Word Single-meaning Embedding Model

We propose a novel algorithm of semantic similarity between words, based on our word single-meaning embedding model, to address the issue of existing word-embedding-based approaches that have low computation accuracy in polysemous words, nonadjacent words and synonyms. Differently from the existing word embedding models, each polysemous word is decomposed into a series of monosemous words in our model, and there is a one-to-one correspondence between a word meaning and a vector. First of all, the word sense disambiguation (WSD) of polysemous words in different contexts of the corpus is achieved with the help of the prior classification information contained in Tongyici Cilin. Then, the word single-meaning embeddings are learned from the processed corpus and realize the precise expression for each word meaning, and as far as we know, no existing word embedding model could complete this task. At last, two test words are decomposed into marked monosemous words according to the number of meaning and expanded with synonyms, and then semantic relatedness between words is computed based on the word single-meaning embedding model and Tongyici Cilin. The experimental results showed our method can significantly improve the computation accuracy of polysemous words, nonadjacent words and synonyms. Copyright © 2020 Acta Automatica Sinica. All rights reserved.