Abstract Entity Coreference Resolution is the task of resolving all mentions in a document
that refer to the same real world entity and is considered as one of the most difficult tasks in …

Correctly resolving textual mentions of people fundamentally entails making inferences
about those people. Such inferences raise the risk of systemic biases in coreference …

The field of Natural Language Processing (NLP) has experienced significant growth in
recent years, largely due to advancements in Deep Learning technology and especially …

Correctly resolving textual mentions of people fundamentally entails making inferences
about those people. Such inferences raise the risk of systematic biases in coreference …

Coreference resolution of geological entities is an important task in geological information
mining. Although the existing generic coreference resolution models can handle geological …

Coreference resolution metrics quantify errors but do not analyze them. Here, we consider
an automated method of categorizing errors in the output of a coreference system into …

Cross-document event coreference resolution (CDCR) is an NLP task in which mentions of
events need to be identified and clustered throughout a collection of documents. CDCR …

Hyperparameters are the foundation for optimizing the way machine learning algorithms
supposed to learn. It is essential to have the optimal hyperparameter values for any learning …

Quantities are more than numeric values. They represent measures for entities, expressed in
numbers with associated units. Search queries often include quantities, such as athletes …

In this chapter, we overview the major efforts in evaluation campaigns (shared tasks) for
coreference resolution, where multiple participants are given the same datasets and …