Anguiano (2013): "We find that multiple-candidate approaches slightly improve parsing accuracy overall as well as for prepositional phrase attachment and coordination, two linguistic phenomena that exhibit high syntactic ambiguity."
Entity linking: Heinzerling et al. (2017)
- Hall, Keith, and Václav Novák. "Corrective modeling for non-projective dependency parsing." In Proceedings of the Ninth International Workshop on Parsing Technology, pp. 42-52. Association for Computational Linguistics, 2005.
- Anguiano, E.H. and Candito, M., 2011, July. Parse correction with specialized models for difficult attachment types. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 1222-1233). Association for Computational Linguistics.
- Cetinoglu, Ozlem, Anton Bryl, Jennifer Foster, and Josef Van Genabith. "Improving dependency label accuracy using statistical post-editing: A cross-framework study." (2011).
- Anguiano, Enrique Henestroza. "Efficient large-context dependency parsing and correction with distributional lexical resources." PhD diss., Université Paris-Diderot-Paris VII, 2013.
- Heinzerling, B., Strube, M., & Lin, C.-Y. (2017). Trust, but Verify! Better Entity Linking through Automatic Verification. In EACL 2017 (pp. 828–838).