Fast and Discriminative Semantic Embedding

By Rob Koopman, Shenghui Wang, and Gwenn Englebienne

We present a novel, effective and efficient method for term and document embedding method. Our experiments show it outperforms state-of-the-art methods in terms of the STS benchmark and subject prediction when trained on the same datasets, while at the same time being computationally cheaper by orders of magnitude.