Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/13799
Title: Incorporation of constraints to improve machine learning approaches on coreference resolution
Authors: CEN CEN
Keywords: coreference resolution, multi-level ranked constraints, conflict resolution, linguistic knowledge, machine learning, MUC-7
Issue Date: 9-Mar-2004
Source: CEN CEN (2004-03-09). Incorporation of constraints to improve machine learning approaches on coreference resolution. ScholarBank@NUS Repository.
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%.
URI: http://scholarbank.nus.edu.sg/handle/10635/13799
Appears in Collections:Master's Theses (Open)

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