SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus
This work addresses the need for better word embeddings in specialized fields like automotive and manufacturing, though it appears incremental as it builds on existing embedding techniques for small corpora.
The paper tackled the problem of limited applicability of existing word embeddings for small domain-specific corpora by proposing a novel method to generate highly interpretable and efficient embeddings, demonstrating enhanced interpretability features in evaluation results.
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.