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
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance GenerationCode1
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
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing0
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction0
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 Typing0
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing0
Prototypical Verbalizer for Prompt-based Few-shot Tuning0
Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction0
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification0
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing0
An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing0
Fine-grained Entity Typing without Knowledge BaseCode0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
Show:102550
← PrevPage 3 of 7Next →

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