Entity Disambiguation
56 papers with code • 10 benchmarks • 11 datasets
Entity Disambiguation is the task of linking mentions of ambiguous entities to their referent entities in a knowledge base such as Wikipedia.
Source: Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
Datasets
Latest papers
Universal Knowledge Graph Embeddings
Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting.
A Read-and-Select Framework for Zero-shot Entity Linking
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.
Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
In this work, we demonstrate that formal grammars can describe the output space for a much wider range of tasks and argue that GCD can serve as a unified framework for structured NLP tasks in general.
Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED).
Disambiguation of Company names via Deep Recurrent Networks
Moreover, we show that Active Learning prioritisation is indeed helpful when labelling resources are limited, and let the learning models reach the out-of-sample performance saturation with less labelled data with respect to standard (random) data labelling approaches.
KILM: Knowledge Injection into Encoder-Decoder Language Models
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters.
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
For that study, we introduce TempEL, an entity linking dataset that consists of time-stratified English Wikipedia snapshots from 2013 to 2022, from which we collect both anchor mentions of entities, and these target entities' descriptions.
Entity Disambiguation with Entity Definitions
Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
Improving Entity Disambiguation by Reasoning over a Knowledge Base
Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types.