Incorporation of constraints to improve machine learning approaches on coreference resolution

Abstract

In this thesis, we utilize linguistic knowledge to improve coreference resolution systems built through a machine learning approach. The improvement is the result of two main ideas: incorporation of multi-level ranked constraints based on linguistic knowledge and conflict resolution for handling conflicting constraints within a set of corefering elements. The method resolves problems with using machine learning for building coreference resolution systems, primarily the problem of having limited amounts of training data. The method provides a bridge between coreference resolution methods built using linguistic knowledge and machine learning methods. It outperforms earlier machine learning approaches on MUC-7 data increasing the F-measure of a baseline system built using a machine learning method from 60.9% to 64.2%.