Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications
Table 4: Comparison between embeddings generated from specific fields
['Embeddings', 'F-Score Computer Science', 'F-Score Mathematics', 'F-Score Chemistry']
In general, embeddings generated from each field train the best classifier for that field. In addition, we note that the level of proximity between knowledge fields appears to be related to these results. For example, embeddings obtained from the Mathematics field perform well in Computer Science and Chemistry, which are both related to Mathematics, and the other way around also holds: embeddings from Computer Science and Chemistry perform well in Mathematics. However, Chemistry embeddings obtain worse results in the Computer Science field and Computer Science embeddings perform worse in the Chemistry category. This could be related to the fact that these fields have less similarities with each other than with Mathematics. Curiously, Computer Science and Chemistry embeddings seem to work even better for Mathematics than for their originating fields, which will need to be further researched.