A Thesis on Character Identification Public
Zhou, Ethan (Spring 2018)
Published
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
About this Honors Thesis
School | |
---|---|
Department | |
Degree | |
Submission | |
Language |
|
Research Field | |
Mot-clé | |
Committee Chair / Thesis Advisor | |
Committee Members |
Primary PDF
Thumbnail | Title | Date Uploaded | Actions |
---|---|---|---|
|
A Thesis on Character Identification () | 2018-04-04 21:53:17 -0400 |
|
Supplemental Files
Thumbnail | Title | Date Uploaded | Actions |
---|