Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications
Table 3: Evaluation results for fine-grained categories in three knowledge fields
['Algorithm', 'Dataset', 'Dim.', 'F-Score Comp. Sci.', 'F-Score Math.', 'F-Score Chem.']
FastText (Wiki+Web+News) embeddings and GloVe (Wiki+Giga) achieve similar f-scores. In this fine-grained classification, we observe that embeddings from the SciGraph general corpus produce average results and do not completely discriminate second-level categories across knowledge fields. This is partially due to differences in corpora size between the general and field-specific cases but also in the semantics captured for each specific word in their corresponding vectors. In the case of the general corpus, the latter is influenced by all the different contexts where a particular word can appear across fields of knowledge. As a consequence, the location in the vector space of the point associated to a particular word is shifted compared to embeddings generated locally to each field.