Syntree2Vec - An algorithm to augment syntactic hierarchy into word embeddings
This work addresses the challenge of creating accurate word embeddings for specialized domains with limited data, though it appears incremental in nature.
The authors tackled the problem of low accuracy in domain-specific word embeddings when training data is scarce by infusing syntactic knowledge into the embeddings, resulting in improved syntactic strength and robust performance on meagre data.
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.