SOTAVerified

Entity Typing

Entity Typing is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).

Source: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Image Credit: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Papers

Showing 51100 of 170 papers

TitleStatusHype
Dense Retrieval as Indirect Supervision for Large-space Decision MakingCode0
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies0
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt TuningCode0
Do Language Models Learn about Legal Entity Types during Pretraining?Code0
Multi-view Contrastive Learning for Entity Typing over Knowledge GraphsCode0
Ontology Enrichment for Effective Fine-grained Entity Typing0
SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features0
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations0
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference ChainsCode0
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy0
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing0
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models0
Dynamic Named Entity RecognitionCode0
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity TypingCode0
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random FieldCode0
Learning to Select from Multiple OptionsCode0
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification0
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity TypingCode0
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing0
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs0
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type InformationCode0
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing0
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions0
MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base0
Conditional set generation using Seq2seq models0
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity TypingCode0
Automatic Noisy Label Correction for Fine-Grained Entity TypingCode0
Unified Semantic Typing with Meaningful Label InferenceCode0
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction0
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?0
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource LanguagesCode0
Cross-lingual Inference with A Chinese Entailment GraphCode0
Prototypical Verbalizer for Prompt-based Few-shot Tuning0
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing0
Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction0
Prompt-Learning for Fine-Grained Entity Typing0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification0
Fine-grained Entity Typing without Knowledge BaseCode0
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing0
Fine-grained Typing of Emerging Entities in Microblogs0
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
Cross-lingual Inference with A Chinese Entailment Graph0
Cross-Lingual Fine-Grained Entity Typing0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification0
Fine-grained Entity Typing via Label Reasoning0
Learning with Different Amounts of Annotation: From Zero to Many LabelsCode0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MLMETF178.2Unverified
2K-Adapter ( fac-adapter )F177.69Unverified
3K-Adapter ( fac-adapter + lin-adapter )F177.61Unverified
4ERNIEF175.56Unverified
5MCCE-B (replicated by Adaseq)F152.1Unverified
6Prompt + NPCRF (replicated by Adaseq)F150.1Unverified
7UniST-LargeF149.9Unverified
8Prompt Learning (replicated by Adaseq))F149.3Unverified
9MLMETF149.1Unverified
10RoBERTa-Large + NPCRF (replicated by Adaseq)F147.3Unverified
#ModelMetricClaimedVerifiedStatus
1MLMETF149.1Unverified
2ELMo (distant denoising data)F140.2Unverified
3LabelGCN Xiong et al. (2019)F136.9Unverified
4Choi et al. (2018) w augmentationF132Unverified
#ModelMetricClaimedVerifiedStatus
1REXELAvg F196.01Unverified
2REXELAvg F190.93Unverified
3REXELAvg F186.74Unverified
#ModelMetricClaimedVerifiedStatus
1ReFinEDMicro-F184Unverified
#ModelMetricClaimedVerifiedStatus
1LITEMacro F180.1Unverified
#ModelMetricClaimedVerifiedStatus
1TextEnt-fullAccuracy37.4Unverified
#ModelMetricClaimedVerifiedStatus
1LITEMacro F186.6Unverified