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The emergence of knowledge graphs in the scholarly communication domain andrecent advances in artificial intelligence and natural language processingbring us closer to a scenario where intelligent systems can assist scientistsover a range of knowledge-intensive tasks. In this paper we presentexperimental results about the generation of word embeddings from scholarlypublications for the intelligent processing of scientific texts extracted fromSciGraph. We compare the performance of domain-specific embeddings withexisting pre-trained vectors generated from very large and general purposecorpora. Our results suggest that there is a trade-off between corpusspecificity and volume. Embeddings from domain-specific scientific corporaeffectively capture the semantics of the domain. On the other hand, obtainingcomparable results through general corpora can also be achieved, but only inthe presence of very large corpora of well formed text. Furthermore, We alsoshow that the degree of overlapping between knowledge areas is directly relatedto the performance of embeddings in domain evaluation tasks.

Not just about size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications