Predictor Feature Ablation Study

Jump to: navigation, search

A Predictor Feature Ablation Study is an empirical analysis task that explores the contribution of predictor features on predictive performance.



  • (Gimpel et al., 2011) ⇒ Kevin Gimpel, Nathan Schneider, Brendan O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan, and Noah A. Smith. (2011). “Part-of-speech tagging for twitter: Annotation, features, and experiments." In" Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies.


  1. We do not have a test with the basic alignment features removed because they are necessary to compute a0(t, t0).