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

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Showing 51–100 of 170 papers

TitleStatusHype
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance GenerationCode1
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
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing—0
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction—0
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource LanguagesCode0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?—0
PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding ReasoningCode1
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
Prototypical Verbalizer for Prompt-based Few-shot TuningCode4
Cross-lingual Inference with A Chinese Entailment GraphCode0
Nested Named Entity Recognition as Latent Lexicalized Constituency ParsingCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
Ultra-fine Entity Typing with Indirect Supervision from Natural Language InferenceCode1
Prompt-Learning for Fine-Grained Entity Typing—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
Prototypical Verbalizer for Prompt-based Few-shot Tuning—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing—0
Fine-grained Entity Typing without Knowledge BaseCode0
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
Fine-grained Typing of Emerging Entities in Microblogs—0
Cross-lingual Inference with A Chinese Entailment Graph—0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification—0
Cross-Lingual Fine-Grained Entity Typing—0
Context-aware Entity Typing in Knowledge GraphsCode1
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
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Prompt-Learning for Fine-Grained Entity Typing—0
CogIE: An Information Extraction Toolkit for Bridging Texts and CogNetCode1
Manifold Alignment across Geometric Spaces for Knowledge Base Representation LearningCode0
Ultra-Fine Entity Typing with Weak Supervision from a Masked Language ModelCode1
Fine-grained General Entity Typing in German using GermaNetCode0
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation ExtractionCode0
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological ConceptsCode1
bbw: Matching CSV to Wikidata via Meta-lookupCode1
LOME: Large Ontology Multilingual Extraction—0
FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space—0
Modeling Fine-Grained Entity Types with Box EmbeddingsCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
Syntax-Enhanced Pre-trained ModelCode1
ManyEnt: A Dataset for Few-shot Entity TypingCode0
Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text—0
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class ClassificationCode1
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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