Abstract
Table 1: Parameters leading to the higher accuracy during the optimisation task.
['dm', 'vector_size', 'sample', 'alpha', 'window', 'hs']
Regarding the optimisation, 1,920 different models were trained and evaluated. First, the dm parameter highly affects the accuracy. Indeed, the PV-DBOW architecture looks more precise with a highest accuracy of 25.78%, while the PV-DM reached only 18.08% of common MeSH terms in average between query and top-close documents. Then, embedding vectors having large number of dimensions (>256) seem to lead to a better accuracy, for PV-DBOW at least. Finally, when set too low (<0.01), the alpha parameter leads to poor accuracy. The best combination of parameters, obtained thanks to the PV-DBOW architecture, was selected. The best parameters regarding the PV-DM, but having the same vector_size value, were also kept (13.30% of accuracy). The concatenation of models is thus possible without dimensions reduction, this method being promoted by Mikolov and Lee [le2014distributed].