Work

Global learning of noun phrase anaphoricity in coreference resolution via label propagation

Year: 2009

Type: conference-paper

Abstract: Knowledge of noun phrase anaphoricity might be profitably exploited in coreference resolution to bypass the resolution of non-anaphoric noun phrases.However, it is surprising to notice that recent attempts to incorporate automatically acquired anaphoricity information into coreference resolution have been somewhat disappointing.This paper employs a global learning method in determining the anaphoricity of noun phrases via a label propagation algorithm to improve learningbased coreference resolution.In particular, two kinds of kernels, i.e. the feature-based RBF kernel and the convolution tree kernel, are employed to compute the anaphoricity similarity between two noun phrases.Experiments on the ACE 2003 corpus demonstrate the effectiveness of our method in anaphoricity determination of noun phrases and its application in learning-based coreference resolution.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.3115/1699571.1699640

Authors: GuoDong Zhou, Fang Kong

Institution: Soochow University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Research Foundation, National Natural Science Foundation of China

Award: 基于树核函数的弱指导实体间语义关系抽取研究