Word embeddings aims to map sense of the words into a lower dimensional vector space in order to reason over them. Training embeddings on domain specific data helps express concepts more relevant to their use case but comes at a cost of accuracy when data is less. Our effort is to minimise this by infusing syntactic knowledge into the embeddings. We propose a graph based embedding algorithm inspired from node2vec. Experimental results have shown that our algorithm improves the syntactic strength and gives robust performance on meagre data.
@article{arxiv.1808.05907,
title = {Syntree2Vec - An algorithm to augment syntactic hierarchy into word embeddings},
author = {Shubham Bhardwaj},
journal= {arXiv preprint arXiv:1808.05907},
year = {2018}
}