autoNLP: NLP Feature Recommendations for Text Analytics Applications
This addresses inefficiencies in feature engineering for NLP data scientists, though it appears incremental as it builds on existing standardization efforts.
The paper tackles the problem of manual feature selection in NLP applications by proposing a standardized language for specifying NLP features and an approach for their reuse across applications, aiming to increase the likelihood of identifying optimal features.
While designing machine learning based text analytics applications, often, NLP data scientists manually determine which NLP features to use based upon their knowledge and experience with related problems. This results in increased efforts during feature engineering process and renders automated reuse of features across semantically related applications inherently difficult. In this paper, we argue for standardization in feature specification by outlining structure of a language for specifying NLP features and present an approach for their reuse across applications to increase likelihood of identifying optimal features.