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 5175 of 170 papers

TitleStatusHype
ManyEnt: A Dataset for Few-shot Entity TypingCode0
A Systematic Study of Leveraging Subword Information for Learning Word RepresentationsCode0
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference ChainsCode0
Modelling Commonsense Commonalities with Multi-Facet Concept EmbeddingsCode0
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity TypingCode0
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity TypingCode0
Exploiting Semantic Relations for Fine-grained Entity TypingCode0
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity TypingCode0
Dynamic Named Entity RecognitionCode0
Do Language Models Learn about Legal Entity Types during Pretraining?Code0
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity TypingCode0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt TuningCode0
Learning to Denoise Distantly-Labeled Data for Entity TypingCode0
Dense Retrieval as Indirect Supervision for Large-space Decision MakingCode0
A Primal Dual Formulation For Deep Learning With ConstraintsCode0
Learning with Different Amounts of Annotation: From Zero to Many LabelsCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 LanguagesCode0
Knowledge Enhanced Contextual Word RepresentationsCode0
KCAT: A Knowledge-Constraint Typing Annotation ToolCode0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
Cross-lingual Inference with A Chinese Entailment GraphCode0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
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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