  The introduction of embedding techniques has pushed forward significantly the
Natural Language Processing field. Many of the proposed solutions have been
presented for word-level encoding; anyhow, in the last years, new mechanism to
treat information at an higher level of aggregation, like at sentence- and
document-level, have emerged. With this work we address specifically the
sentence embeddings problem, presenting the Static Fuzzy Bag-of-Word model. Our
model is a refinement of the Fuzzy Bag-of-Words approach, providing sentence
embeddings with a predefined dimension. SFBoW provides competitive performances
in Semantic Textual Similarity benchmarks, while requiring low computational
resources.
