  In essence, embedding algorithms work by optimizing the distance between a
word and its usual context in order to generate an embedding space that encodes
the distributional representation of words. In addition to single words or word
pieces, other features which result from the linguistic analysis of text,
including lexical, grammatical and semantic information, can be used to improve
the quality of embedding spaces. However, until now we did not have a precise
understanding of the impact that such individual annotations and their possible
combinations may have in the quality of the embeddings. In this paper, we
conduct a comprehensive study on the use of explicit linguistic annotations to
generate embeddings from a scientific corpus and quantify their impact in the
resulting representations. Our results show how the effect of such annotations
in the embeddings varies depending on the evaluation task. In general, we
observe that learning embeddings using linguistic annotations contributes to
achieve better evaluation results.
