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
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Prompt-Learning for Fine-Grained Entity Typing0
Manifold Alignment across Geometric Spaces for Knowledge Base Representation LearningCode0
Fine-grained General Entity Typing in German using GermaNetCode0
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation ExtractionCode0
LOME: Large Ontology Multilingual Extraction0
FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space0
ManyEnt: A Dataset for Few-shot Entity TypingCode0
Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text0
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts0
Zero-Shot Learning with Common Sense Knowledge Graphs0
Interpretable Entity Representations through Large-Scale Typing0
Entity Type Prediction in Knowledge Graphs using Embeddings0
Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings0
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations0
MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing0
E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings0
Exploiting Semantic Relations for Fine-grained Entity TypingCode0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension0
Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model0
A Primal Dual Formulation For Deep Learning With ConstraintsCode0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension0
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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