  A novel sentence embedding method built upon semantic subspace analysis,
called semantic subspace sentence embedding (S3E), is proposed in this work.
Given the fact that word embeddings can capture semantic relationship while
semantically similar words tend to form semantic groups in a high-dimensional
embedding space, we develop a sentence representation scheme by analyzing
semantic subspaces of its constituent words. Specifically, we construct a
sentence model from two aspects. First, we represent words that lie in the same
semantic group using the intra-group descriptor. Second, we characterize the
interaction between multiple semantic groups with the inter-group descriptor.
The proposed S3E method is evaluated on both textual similarity tasks and
supervised tasks. Experimental results show that it offers comparable or better
performance than the state-of-the-art. The complexity of our S3E method is also
much lower than other parameterized models.
