  Nowadays, search engine users commonly rely on query suggestions to improve
their initial inputs. Current systems are very good at recommending lexical
adaptations or spelling corrections to users' queries. However, they often
struggle to suggest semantically related keywords given a user's query. The
construction of a detailed query is crucial in some tasks, such as legal
retrieval or academic search. In these scenarios, keyword suggestion methods
are critical to guide the user during the query formulation. This paper
proposes two novel models for the keyword suggestion task trained on scientific
literature. Our techniques adapt the architecture of Word2Vec and FastText to
generate keyword embeddings by leveraging documents' keyword co-occurrence.
Along with these models, we also present a specially tailored negative sampling
approach that exploits how keywords appear in academic publications. We devise
a ranking-based evaluation methodology following both known-item and ad-hoc
search scenarios. Finally, we evaluate our proposals against the
state-of-the-art word and sentence embedding models showing considerable
improvements over the baselines for the tasks.
