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Word embeddings are a basic building block of modern NLP pipelines. Effortshave been made to learn rich, efficient, and interpretable embeddings for largegeneric datasets available in the public domain. However, these embeddings havelimited applicability for small corpora from specific domains such asautomotive, manufacturing, maintenance and support, etc. In this work, wepresent a comprehensive notion of interpretability for word embeddings andpropose a novel method to generate highly interpretable and efficientembeddings for a domain-specific small corpus. We report the evaluation resultsof our resulting word embeddings and demonstrate their novel features forenhanced interpretability.

SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus