For the study, researchers investigated the application of two machine-learning algorithms: gradient tree boosting and LSTM-based deep learning.

Gradient tree boosting uses a small sample of features to measure similarities between potential answers and any given mention.

XGBoost (eXtreme Gradient Boosting), also not a deep neural network, is a scalable, end-to-end tree boosting system that has produced state-of-the-art results on many machine learning challenges.

The study tested four machine learning algorithms to analyze the data: Gradient tree boosting, random forest, classification and regression trees, or CART, and support vector machine.