Relational feature engineering of natural language processing

We present a new framework for feature engineering of natural language processing that is based on a relational data model of text. It includes fast and flexible methods for implementing and extracting new features and thereby reduces the effort of creating an NLP system for a particular task. In an instantiation and evaluation of the framework for the problem of coreference resolution in multiple languages, we were able to obtain competitive results in a short implementation period. This demonstrates the potential power of our framework for feature engineering.