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 101125 of 170 papers

TitleStatusHype
Collective Learning From Diverse Datasets for Entity Typing in the Wild0
Automatic lexical semantic classification of nouns0
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)0
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension0
ConcEPT: Concept-Enhanced Pre-Training for Language Models0
Conditional set generation using Seq2seq models0
Corpus-level Fine-grained Entity Typing0
Corpus-level Fine-grained Entity Typing Using Contextual Information0
COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing0
Cross-Lingual Fine-Grained Entity Typing0
Cross-lingual Inference with A Chinese Entailment Graph0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing0
Description-Based Zero-shot Fine-Grained Entity Typing0
Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction0
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction0
E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings0
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs0
Entity Type Prediction in Knowledge Graphs using Embeddings0
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions0
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts0
Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation0
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks0
Fine-grained Entity Typing via Label Reasoning0
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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