  Word embeddings are a basic building block of modern NLP pipelines. Efforts
have been made to learn rich, efficient, and interpretable embeddings for large
generic datasets available in the public domain. However, these embeddings have
limited applicability for small corpora from specific domains such as
automotive, manufacturing, maintenance and support, etc. In this work, we
present a comprehensive notion of interpretability for word embeddings and
propose a novel method to generate highly interpretable and efficient
embeddings for a domain-specific small corpus. We report the evaluation results
of our resulting word embeddings and demonstrate their novel features for
enhanced interpretability.

Computation and Language;Machine Learning