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 126150 of 170 papers

TitleStatusHype
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity TypingCode0
Ultra-Fine Entity TypingCode0
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference ChainsCode0
A Systematic Study of Leveraging Subword Information for Learning Word RepresentationsCode0
Zero-Shot Open Entity Typing as Type-Compatible GroundingCode0
Dynamic Named Entity RecognitionCode0
From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grainedCode0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity TypingCode0
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 LanguagesCode0
Improving Fine-grained Entity Typing with Entity LinkingCode0
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type InformationCode0
Fine-grained General Entity Typing in German using GermaNetCode0
Automatic Noisy Label Correction for Fine-Grained Entity TypingCode0
Fine-grained Entity Typing without Knowledge BaseCode0
KCAT: A Knowledge-Constraint Typing Annotation ToolCode0
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language RepresentationCode0
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained RelationshipsCode0
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge BasesCode0
Knowledge Enhanced Contextual Word RepresentationsCode0
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification ThresholdsCode0
Fine-Grained Entity Typing in Hyperbolic SpaceCode0
Do Language Models Learn about Legal Entity Types during Pretraining?Code0
Fine-Grained Entity Typing for Domain Independent Entity LinkingCode0
Unified Semantic Typing with Meaningful Label InferenceCode0
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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