We propose a new word embedding model, called SPhrase, that incorporates supervised phrase information. Our method modifies traditional word embeddings by ensuring that all target words in a phrase have exactly the same context. We demonstrate that including this information within a context window produces superior embeddings for both intrinsic evaluation tasks and downstream extrinsic tasks.
@article{arxiv.2002.06450,
title = {Supervised Phrase-boundary Embeddings},
author = {Manni Singh and David Weston and Mark Levene},
journal= {arXiv preprint arXiv:2002.06450},
year = {2020}
}