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
Automatic lexical semantic classification of nouns—0
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 Classification—0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension—0
ConcEPT: Concept-Enhanced Pre-Training for Language Models—0
Conditional set generation using Seq2seq models—0
Corpus-level Fine-grained Entity Typing—0
Corpus-level Fine-grained Entity Typing Using Contextual Information—0
COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing—0
Cross-Lingual Fine-Grained Entity Typing—0
Cross-lingual Inference with A Chinese Entailment Graph—0
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing—0
Description-Based Zero-shot Fine-Grained Entity Typing—0
Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction—0
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction—0
E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings—0
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs—0
Entity Type Prediction in Knowledge Graphs using Embeddings—0
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions—0
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts—0
Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings—0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation—0
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks—0
Fine-grained Entity Typing via Label Reasoning—0
Fine-Grained Entity Typing with High-Multiplicity Assignments—0
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations—0
FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space—0
Fine-grained Typing of Emerging Entities in Microblogs—0
Finer Grained Entity Typing with TypeNet—0
FINET: Context-Aware Fine-Grained Named Entity Typing—0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?—0
Improving Distantly-supervised Entity Typing with Compact Latent Space Clustering—0
Interpretable Entity Representations through Large-Scale Typing—0
Knowledge-Aware Conversational Semantic Parsing Over Web Tables—0
Label Embedding for Zero-shot Fine-grained Named Entity Typing—0
Learning Entity Representations for Few-Shot Reconstruction of Wikipedia Categories—0
Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing—0
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification—0
LOME: Large Ontology Multilingual Extraction—0
MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base—0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities—0
Multi-source named entity typing for social media—0
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing—0
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing—0
Noise Mitigation for Neural Entity Typing and Relation Extraction—0
Nonsymbolic Text Representation—0
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages—0
Ontology Enrichment for Effective Fine-grained Entity Typing—0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension—0
Path-Based Attention Neural Model for Fine-Grained Entity Typing—0
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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