A matching method of oral text to instruction based on word vector
Ziheng Gao, Zhiqiang Fan · 5th International Conference on Computer Information Science and Application Technology (CISAT 2022) · 2022
Short oral texts have the characteristics of sparse features and vague expressions, which lead to poor performance when applying matching methods to them. Aiming at these problems, this paper proposes a matching method based on word vector text representation, which comprehensively considers part-of-speech, semantics and word order to map oral text to operation instructions. First, this paper uses the Skip-gram model to train the selected corpus to obtain the word vector representation of the corpus; then uses the cosine similarity to calculate the similarity of the feature words; then, combined with the text characteristics in this scene, use the WMR(Word Matching Rate) to calculate the similarity between short texts; finally, the method in this paper is evaluated on the test set. The results show that the method described in this paper has better precision and recall rate than other methods, and effectively improves the matching of oral text to instruction text.