Entity Linking
213 papers with code • 23 benchmarks • 35 datasets
Assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text (Source: Wikipedia).
Benchmarks
These leaderboards are used to track progress in Entity Linking
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Libraries
Use these libraries to find Entity Linking models and implementations
FUNSD
FIGER
KILT
AIDA CoNLL-YAGO
MedMentions
IPM NEL
Most implemented papers
Named Entity Recognition with Bidirectional LSTM-CNNs
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.
Zero-Shot Entity Linking by Reading Entity Descriptions
First, we show that strong reading comprehension models pre-trained on large unlabeled data can be used to generalize to unseen entities.
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.
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.
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.
Visual FUDGE: Form Understanding via Dynamic Graph Editing
herobd/FUDGE
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FUDGE edits the graph structure by combining text segments (graph vertices) and pruning edges in an iterative fashion to obtain the final text entities and relationships.
Improving Entity Linking by Modeling Latent Relations between Mentions
lephong/mulrel-nel
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ACL 2018
Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base.
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies.
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking
To the best of our knowledge, this is the first time that plural mentions are thoroughly analyzed for these two resolution tasks.
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.
