Chao Ma


Joint Neural Entity Disambiguation with Output Space Search
Hamed Shahbazi | Xiaoli Fern | Reza Ghaeini | Chao Ma | Rasha Mohammad Obeidat | Prasad Tadepalli
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we start from a complete solution constructed by a local model and conduct a search in the space of possible corrections to improve the local solution from a global view point. Our search utilizes a heuristic function to focus more on the least confident local decisions and a pruning function to score the global solutions based on their local fitness and the global coherences among the predicted entities. Experimental results on CoNLL 2003 and TAC 2010 benchmarks verify the effectiveness of our model.


Improving Users’ Demographic Prediction via the Videos They Talk about
Yuan Wang | Yang Xiao | Chao Ma | Zhen Xiao
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


Prune-and-Score: Learning for Greedy Coreference Resolution
Chao Ma | Janardhan Rao Doppa | J. Walker Orr | Prashanth Mannem | Xiaoli Fern | Tom Dietterich | Prasad Tadepalli
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)