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 2650 of 170 papers

TitleStatusHype
Connecting Embeddings for Knowledge Graph Entity TypingCode1
Context-aware Entity Typing in Knowledge GraphsCode1
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label EmbeddingCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
K-Adapter: Infusing Knowledge into Pre-Trained Models with AdaptersCode1
KoCHET: a Korean Cultural Heritage corpus for Entity-related TasksCode1
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentionCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
Kuaipedia: a Large-scale Multi-modal Short-video EncyclopediaCode1
bbw: Matching CSV to Wikidata via Meta-lookupCode1
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance GenerationCode1
PromptNER: Prompt Locating and Typing for Named Entity RecognitionCode1
μKG: A Library for Multi-source Knowledge Graph Embeddings and ApplicationsCode1
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological ConceptsCode1
KCAT: A Knowledge-Constraint Typing Annotation ToolCode0
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity TypingCode0
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge BasesCode0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
A Systematic Study of Leveraging Subword Information for Learning Word RepresentationsCode0
Improving Fine-grained Entity Typing with Entity LinkingCode0
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference ChainsCode0
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity TypingCode0
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type InformationCode0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
Dynamic Named Entity RecognitionCode0
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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