Neural methods for embedding entities are typically extrinsically evaluated on downstream tasks and, more recently, intrinsically using probing tasks.

Nov 17, 2020 · We address both of these issues by evaluating a diverse set of eight neural entity embedding methods on a set of simple probing tasks.

We evaluate three pretrained embedding models that leverage context and graph-based information to represent entities. For all three models, we train. 300 ...

This work evaluates a diverse set of eight neural entity embedding methods on a set of simple probing tasks, demonstrating which methods are able to ...

Request PDF | On Jan 1, 2020, Andrew Runge and others published Exploring Neural Entity Representations for Semantic Information | Find, read and cite all ...

Nov 29, 2020 · Knowledge about personally familiar people and places is extremely rich and varied, involving pieces of semantic information connected in ...

Jul 30, 2024 · In this paper, we explore the semantic enhancement of NER in ancient Chinese books through the utilization of external knowledge. We propose a ...

May 17, 2021 · Named Entity Recognition is a common task in Natural Language Processing applications, whose purpose is to recognize named entities in ...

Oct 22, 2024 · We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both ...

We introduce a neural "taxonomical" semantic parser to utilize this new representation system of predicates, and compare it with a standard neural semantic ...