Comparison of Semantic Similarity Models on Constrained Scenarios

Abstract

The technological world has grown by incorporating billions of small sensing devices, collecting and sharing large amounts of diversified data over the new generation of wireless and mobile networks. We can use semantic similarity models to help organize and optimize these devices. Even so, many of the proposed semantic similarity models do not consider the constrained and dynamic environments where these devices are present (IoT, edge computing, 5g, and next-generation networks). In this paper, we review the commonly used models, discuss the limitations of our previous model, and explore latent space methods (through matrix factorization) to reduce noise and correct the model profiles with no additional data. The new proposal is evaluated with corpus-based state-of-the-art approaches achieving competitive results while having four times faster training time than the next fastest model and occupying 36 times less disk space than the next smallest model.