A Thesis on Character Identification 公开

Zhou, Ethan (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/np1939186?locale=zh
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Abstract

Traditional coreference resolution systems use methods insufficient for completely resolving 

plural mentions, especially when applying conventional coreference concepts to 

different tasks such as character identification. This paper gives a comprehensive view of 

one of the least examined yet most difficult parts of entity resolution–particularly coreference 

resolution and entity linking. Since our approach to entity resolution focuses on its 

applicability to character identification, we use the character identification corpus from 

SemEval 2018 and expand the dataset in scope to include plural mention annotations. We 

then show the inadequacy of these concepts and show an innovative design to overcome 

the shortcomings of traditional coreference ideas for the character identification task in 

this paper. Our innovative design includes an all-new algorithm for coreference resolution 

that selectively creates clusters to handle all types of mentions, singular and plural, as 

well as a new joint deep learning approach to entity linking determine the entities for both 

singular and plural mentions as well. Using our novel design, we demonstrate that our 

coreference and entity linking models surpass more traditional models. To the extent of 

what we know, we are the first to extensively investigate plural mentions in the context of 

entity resolution.

Table of Contents

Introduction Related Work Background Corpus Definitions Data Schema Annotation Crowdsourcing Quality Control Analytics Approach Coreference Resolution Algorithm Evaluation Metrics Entity Linking Multi-Task Learning Evaluation Metrics Experiments Configuration Coreference Resolution Entity Linking Conclusion References

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