Neurofuzzy semantic similarity measurement

Abstract

Automatically identifying the degree of semantic similarity between two small pieces of text has grown in importance recently. Its impact on various computer-related domains and recent breakthroughs in neural computation has increased the opportunities for better solutions to be developed. This work contributes a neurofuzzy approach for semantic textual similarity that uses neural networks and fuzzy logics. The idea is to combine the capabilities of the deep neural models for working with text with the ones from fuzzy logic for aggregating numerical data. The results of our experiments suggest that such an approach can accurately determine semantic similarity.