A key component of deep learning (DL) for natural language processing (NLP) is word
embeddings. Word embeddings that effectively capture the meaning and context of the word
that they represent can significantly improve the performance of downstream DL models for
various NLP tasks. Many existing word embeddings techniques capture the context of words
based on word co-occurrence in documents and text; however, they often cannot capture
broader domain-specific relationships between concepts that may be crucial for the NLP�…