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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 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 Vocabularies—0
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 Typing—0
SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features—0
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations—0
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 Strategy—0
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing—0
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models—0
Dynamic Named Entity RecognitionCode0
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing—0
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field—0
Learning to Select from Multiple OptionsCode0
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification—0
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity TypingCode0
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation—0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing—0
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs—0
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type InformationCode0
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing—0
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions—0
MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base—0
Conditional set generation using Seq2seq models—0
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 Correction—0
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing—0
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 Tuning—0
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing—0
Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction—0
Prompt-Learning for Fine-Grained Entity Typing—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
Fine-grained Entity Typing without Knowledge BaseCode0
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing—0
Fine-grained Typing of Emerging Entities in Microblogs—0
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing—0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
Cross-lingual Inference with A Chinese Entailment Graph—0
Cross-Lingual Fine-Grained Entity Typing—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
Fine-grained Entity Typing via Label Reasoning—0
Learning with Different Amounts of Annotation: From Zero to Many LabelsCode0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation—0
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All datasetsOpen EntityOntonotes v5 (English)DocRED-IEAIDA-CoNLLFIGERFreebase FIGEROntoNotes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MLMETF178.2—Unverified
2K-Adapter ( fac-adapter )F177.69—Unverified
3K-Adapter ( fac-adapter + lin-adapter )F177.61—Unverified
4ERNIEF175.56—Unverified
5MCCE-B (replicated by Adaseq)F152.1—Unverified
6Prompt + NPCRF (replicated by Adaseq)F150.1—Unverified
7UniST-LargeF149.9—Unverified
8Prompt Learning (replicated by Adaseq)）F149.3—Unverified
9MLMETF149.1—Unverified
10RoBERTa-Large + NPCRF (replicated by Adaseq)F147.3—Unverified
#ModelMetricClaimedVerifiedStatus
1MLMETF149.1—Unverified
2ELMo (distant denoising data)F140.2—Unverified
3LabelGCN Xiong et al. (2019)F136.9—Unverified
4Choi et al. (2018) w augmentationF132—Unverified
#ModelMetricClaimedVerifiedStatus
1REXELAvg F196.01—Unverified
2REXELAvg F190.93—Unverified
3REXELAvg F186.74—Unverified
#ModelMetricClaimedVerifiedStatus
1ReFinEDMicro-F184—Unverified
#ModelMetricClaimedVerifiedStatus
1LITEMacro F180.1—Unverified
#ModelMetricClaimedVerifiedStatus
1TextEnt-fullAccuracy37.4—Unverified
#ModelMetricClaimedVerifiedStatus
1LITEMacro F186.6—Unverified