SOTAVerified

Entity Resolution

Entity resolution (also known as entity matching, record linkage, or duplicate detection) is the task of finding records that refer to the same real-world entity across different data sources (e.g., data files, books, websites, and databases). (Source: Wikipedia)

Surveys on entity resolution:

The task of entity resolution is closely related to the task of entity alignment which focuses on matching entities between knowledge bases. The task of entity linking differs from entity resolution as entity linking focuses on identifying entity mentions in free text.

Papers

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Showing 51–100 of 184 papers

TitleStatusHype
BanglaBioMed: A Biomedical Named-Entity Annotated Corpus for Bangla (Bengali)Code0
A Survey on Efficient Processing of Similarity Queries over Neural Embeddings—0
Analyzing how BERT performs entity matchingCode0
A Critical Re-evaluation of Neural Methods for Entity AlignmentCode1
Effective Explanations for Entity Resolution ModelsCode0
Supervised Contrastive Learning for Product MatchingCode1
Why the Rich Get Richer? On the Balancedness of Random Partition Models—0
Active Gradual Machine Learning for Entity ResolutionCode0
Unsupervised Matching of Data and TextCode0
Graph-based hierarchical record clustering for unsupervised entity resolution—0
Multidimensional Assignment Problem for multipartite entity resolution—0
Combining Data-driven Supervision with Human-in-the-loop Feedback for Entity Resolution—0
A Weak Self-supervision with Transition-Based Modeling for Reference Resolution—0
Em-K Indexing for Approximate Query Matching in Large-scale ER—0
Resources and Evaluations for Danish Entity Resolution—0
FairER: Entity Resolution with Fairness ConstraintsCode0
Cross-Language Learning for Entity MatchingCode0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs—0
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
Towards a Query-Optimal and Time-Efficient Algorithm for Clustering with a Faulty Oracle—0
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and BeyondCode1
Dual-Objective Fine-Tuning of BERT for Entity MatchingCode1
xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem—0
Entity Resolution in Open-domain Conversations—0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems—0
CEREC: A Corpus for Entity Resolution in Email ConversationsCode0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
Deep Indexed Active Learning for Matching Heterogeneous Entity RepresentationsCode1
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations—0
Generating automatically labeled data for author name disambiguation: An iterative clustering method—0
EAGER: Embedding-Assisted Entity Resolution for Knowledge GraphsCode0
Active Deep Learning on Entity Resolution by Risk Sampling—0
ErGAN: Generative Adversarial Networks for Entity Resolution—0
Adaptive Deep Learning for Entity Resolution by Risk Analysis—0
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation—0
Graph-based Aspect Representation Learning for Entity Resolution—0
Cost-effective Variational Active Entity Resolution—0
Biomedical Named Entity Recognition at ScaleCode0
Profiling Entity Matching Benchmark TasksCode0
Automated Metadata Harmonization Using Entity Resolution & Contextual Embedding—0
A Deep Learning Approach to Geographical Candidate Selection through Toponym MatchingCode1
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage—0
Simulating Name-like Vectors for Testing Large-scale Entity Resolution—0
Intermediate Training of BERT for Product MatchingCode1
(Almost) All of Entity Resolution—0
A Study on Entity Resolution for Email Conversations—0
Author Name Disambiguation in Bibliographic Databases: A Survey—0
Deep Entity Matching with Pre-Trained Language ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt4-0613_fewshot-10F1 (%)85.21—Unverified
2gpt-4o-mini-2024-07-18_fine_tunedF1 (%)80.25—Unverified
3RoBERTa-SupConF1 (%)79.28—Unverified
4RobEMF1 (%)79.06—Unverified
5Random ForestF1 (%)79—Unverified
6HGF1 (%)76.4—Unverified
7DittoF1 (%)75.58—Unverified
8CorDEL-SumF1 (%)70.2—Unverified
9DeepMatcher - HybridF1 (%)69.3—Unverified
10D-HATF1 (%)67.5—Unverified
#ModelMetricClaimedVerifiedStatus
1gpt4-0613_zeroshotF1 (%)95.78—Unverified
2RoBERTa-SupConF1 (%)94.29—Unverified
3gpt-4o-mini-2024-07-18_fine_tunedF1 (%)94.09—Unverified
4gpt-4o-2024-08-06F1 (%)92.2—Unverified
5RobEMF1 (%)90.9—Unverified
6HGF1 (%)89.8—Unverified
7DittoF1 (%)89.33—Unverified
8gpt-4o-mini-2024-07-18F1 (%)87.68—Unverified
9Meta-Llama-3.1-8B-Instruct_fine_tunedF1 (%)87.34—Unverified
10Random ForestF1 (%)85—Unverified
#ModelMetricClaimedVerifiedStatus
1gpt4-0613_zeroshotF1 (%)89.61—Unverified
2gpt-4o-2024-08-06_fine_tuned_wdc_smallF1 (%)87.1—Unverified
3gpt-4o-mini-2024-07-18_structured_explanationsF1 (%)84.38—Unverified
4gpt-4o-mini-2024-07-18F1 (%)81.61—Unverified
5RoBERTa-SupConF1 (%)79.99—Unverified
6Llama3.1_70B_structured_explanationsF1 (%)76.7—Unverified
7Llama3.1_70BF1 (%)75.2—Unverified
8Llama3.1_8B_error-based_example_selectionF1 (%)74.37—Unverified
9Llama3.1_8B_structured_explanationsF1 (%)74.13—Unverified
10DittoF1 (%)73.93—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTF1 (%)96.53—Unverified
2RoBERTa-SupConF1 (%)95.21—Unverified
3HGF1 (%)88.5—Unverified
4DADER-MMDF1 (%)88—Unverified
5DittoF1 (%)80.76—Unverified
6JointBERTF1 (%)77.55—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-SupConF1 (%)98.33—Unverified
2JointBERTF1 (%)97.49—Unverified
3BERTF1 (%)97.37—Unverified
4HGF1 (%)96.5—Unverified
5DittoF1 (%)95.45—Unverified
6Random ForestF1 (%)78—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-baseF1 (%)71.14—Unverified
2DittoF1 (%)70.66—Unverified
3HGF1 (%)68.74—Unverified
4RoBERTa-SupConF1 (%)57.23—Unverified
#ModelMetricClaimedVerifiedStatus
1HGF1 (%)94—Unverified
2DADER-NoDAF1 (%)88.6—Unverified
3DittoF1 (%)85.12—Unverified
4JointBERTF1 (%)75.83—Unverified
#ModelMetricClaimedVerifiedStatus
1ALMSER-GBF10.95—Unverified
2FAMER-SplitMergeF10.88—Unverified
3FAMER-SplitF10.84—Unverified
#ModelMetricClaimedVerifiedStatus
1JointBERTF1 (%)97.09—Unverified
2DittoF1 (%)96.53—Unverified
3HGF1 (%)96.5—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-SupConF1 Micro88.63—Unverified
2RoBERTa-baseF1 Micro52.03—Unverified
#ModelMetricClaimedVerifiedStatus
1gpt-4o-2024-08-06_fine_tuned_wdc_smallF1 (%)87.07—Unverified