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

Universal Knowledge Graph Embeddings

dice-group/universal_embeddings 23 Oct 2023

Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting.

4
23 Oct 2023

A Read-and-Select Framework for Zero-shot Entity Linking

hitsz-tmg/read-and-select 19 Oct 2023

Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.

5
19 Oct 2023

Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning

uiuc-focal-lab/syncode 23 May 2023

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.

66
23 May 2023

Exploring Partial Knowledge Base Inference in Biomedical Entity Linking

yuanhy1997/partialkb-el 18 Mar 2023

Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED).

4
18 Mar 2023

Disambiguation of Company names via Deep Recurrent Networks

rcrupiisp/siamesedisambiguation 7 Mar 2023

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.

2
07 Mar 2023

KILM: Knowledge Injection into Encoder-Decoder Language Models

alexa/kilm 17 Feb 2023

Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters.

23
17 Feb 2023

TempEL: Linking Dynamically Evolving and Newly Emerging Entities

klimzaporojets/tempel 5 Feb 2023

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.

5
05 Feb 2023

Entity Disambiguation with Entity Definitions

sapienzanlp/extend 11 Oct 2022

Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions.

163
11 Oct 2022

ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking

alexa/refined NAACL (ACL) 2022

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.

154
08 Jul 2022

Improving Entity Disambiguation by Reasoning over a Knowledge Base

alexa/refined NAACL 2022

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.

154
08 Jul 2022