  Sentences are important semantic units of natural language. A generic,
distributional representation of sentences that can capture the latent
semantics is beneficial to multiple downstream applications. We observe a
simple geometry of sentences -- the word representations of a given sentence
(on average 10.23 words in all SemEval datasets with a standard deviation 4.84)
roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this
observation, we represent a sentence by the low-rank subspace spanned by its
word vectors. Such an unsupervised representation is empirically validated via
semantic textual similarity tasks on 19 different datasets, where it
outperforms the sophisticated neural network models, including skip-thought
vectors, by 15% on average.
