Abstract

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.

Highlights

  • Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task

  • The goal is to cluster expressions that refer to the same entity or event in a text, whether within a single document or across a document collection

  • Variants of the task differ on two axes: (1) resolving entities (“Duchess of Sussex”, “Meghan Markle”, “she”) vs. events (“Nobel prize for physics [goes to] Donna Strickland”, “Donna Strickland [is awarded] the 2018 Nobel prize for physics”), and (2) whether coreferring mentions occur within a single document (WD: within-document) or across a document collection (CD: cross-document)

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Summary

Introduction

Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. There has been increasing interest in crosstext inferences, for example in question answering (Welbl et al, 2018; Yang et al, 2018; Khashabi et al, 2018; Postma et al, 2018) Such applications would benefit from effective cross-document coreference resolution. Variants of the task differ on two axes: (1) resolving entities (“Duchess of Sussex”, “Meghan Markle”, “she”) vs events (“Nobel prize for physics [goes to] Donna Strickland”, “Donna Strickland [is awarded] the 2018 Nobel prize for physics”), and (2) whether coreferring mentions occur within a single document (WD: within-document) or across a document collection (CD: cross-document). The annotation is not exhaustive, where only a number of salient events and entities in each topic are annotated

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