Entity Linking
109 papers with code • 16 benchmarks • 18 datasets
Assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text (Source: Wikipedia).
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FIGER
AIDA CoNLL-YAGO
KILT
MedMentions
MEIR
FreebaseQA
Greatest papers with code
Named Entity Recognition with Bidirectional LSTM-CNNs
zalandoresearch/flair
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•
TACL 2016
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
Ranked #18 on
Named Entity Recognition
on Ontonotes v5 (English)
Robustness Gym: Unifying the NLP Evaluation Landscape
makcedward/nlpaug
•
•
NAACL 2021
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems.
ERNIE: Enhanced Language Representation with Informative Entities
thunlp/ERNIE
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ACL 2019
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
Ranked #1 on
Entity Linking
on FIGER
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.
Ranked #4 on
Entity Disambiguation
on TAC2010
Efficient One-Pass End-to-End Entity Linking for Questions
facebookresearch/BLINK
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EMNLP 2020
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass.
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
facebookresearch/BLINK
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EMNLP 2020
This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.
DeepType: Multilingual Entity Linking by Neural Type System Evolution
The wealth of structured (e. g. Wikidata) and unstructured data about the world available today presents an incredible opportunity for tomorrow's Artificial Intelligence.
Ranked #1 on
Entity Disambiguation
on TAC2010
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
Ranked #3 on
Entity Linking
on KILT: WNED-CWEB
Multilingual Autoregressive Entity Linking
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Autoregressive Entity Retrieval
facebookresearch/GENRE
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ICLR 2021
For instance, Encyclopedias such as Wikipedia are structured by entities (e. g., one per Wikipedia article).
Ranked #1 on
Entity Linking
on Derczynski