A Simple but Tough-to-Beat Baseline for Sentence Embeddings (2017)(About) > Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD
See also [youtube: Sanjeev Arora on "A theoretical approach to semantic representations"](https://www.youtube.com/watch?v=KR46z_V0BVw)
Semantics with Dense Vectors(About) > We will introduce three methods of generating very dense, short vectors:
> 1. using dimensionality reduction methods like SVD,
> 2. using neural nets like the popular skip-gram or CBOW approaches.
> 3. a quite different approach based on neighboring words called Brown clustering.