  Distributed representations of words, better known as word embeddings, have
become important building blocks for natural language processing tasks.
Numerous studies are devoted to transferring the success of unsupervised word
embeddings to sentence embeddings. In this paper, we introduce a simple
representation of sentences in which a sentence embedding is represented as a
weighted average of word vectors followed by a soft projection. We demonstrate
the effectiveness of this proposed method on the clinical semantic textual
similarity task of the BioCreative/OHNLP Challenge 2018.
