Using Decision Trees for Coreference Resolution
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Learning. Pages 1050-1054.
Abstract
This paper describes RESOLVE, a system that uses decision trees to learn how to classify coreferent phrases in the domain of business joint ventures. An experiment is presented in which the performance of RESOLVE is compared to the performance of a manually engineered set of rules for the same task. The results show that decision trees achieve higher performance than the rules in two of three evaluation metrics developed for the coreference task. In addition to achieving better performance than the rules, RESOLVE provides a framework that facilitates the exploration of the types of knowledge that are useful for solving the coreference problem.
@inproceedings{ijcai1995p136,
title = {Using Decision Trees for Coreference Resolution},
author = {McCarthy, Joseph F. and Lehnert, Wendy G.},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1050--1054},
year = {1995},
note = {Learning},
url = {https://ijcai.org/proceedings/1995/136},
}
TY - CPAPER TI - Using Decision Trees for Coreference Resolution AU - McCarthy, Joseph F. AU - Lehnert, Wendy G. T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1050 EP - 1054 UR - https://ijcai.org/proceedings/1995/136 ER -Copied
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