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 151170 of 170 papers

TitleStatusHype
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension0
Path-Based Attention Neural Model for Fine-Grained Entity Typing0
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model0
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian0
Prompt-Learning for Fine-Grained Entity Typing0
Prompt-Learning for Fine-Grained Entity Typing0
Prototypical Verbalizer for Prompt-based Few-shot Tuning0
Redcoat: A Collaborative Annotation Tool for Hierarchical Entity Typing0
Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text0
SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver0
SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features0
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation0
Transforming Wikipedia into a Large-Scale Fine-Grained Entity Type Corpus0
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing0
Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategy0
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models0
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies0
Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing0
Zero-Shot Learning with Common Sense Knowledge Graphs0
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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