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 2650 of 170 papers

TitleStatusHype
Connecting Embeddings for Knowledge Graph Entity TypingCode1
Context-aware Entity Typing in Knowledge GraphsCode1
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance GenerationCode1
Generative Entity Typing with Curriculum LearningCode1
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label EmbeddingCode1
GeoLM: Empowering Language Models for Geospatially Grounded Language UnderstandingCode1
K-Adapter: Infusing Knowledge into Pre-Trained Models with AdaptersCode1
Hierarchical Entity Typing via Multi-level Learning to RankCode1
Hierarchical Losses and New Resources for Fine-grained Entity Typing and LinkingCode1
bbw: Matching CSV to Wikidata via Meta-lookupCode1
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive LearningCode1
Modeling Fine-Grained Entity Types with Box EmbeddingsCode1
Kuaipedia: a Large-scale Multi-modal Short-video EncyclopediaCode1
Syntax-Enhanced Pre-trained ModelCode1
Corpus-level Fine-grained Entity Typing Using Contextual Information0
Corpus-level Fine-grained Entity Typing0
Collective Learning From Diverse Datasets for Entity Typing in the Wild0
An Attentive Fine-Grained Entity Typing Model with Latent Type Representation0
FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space0
Fine-grained Typing of Emerging Entities in Microblogs0
Conditional set generation using Seq2seq models0
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations0
Enhancing Contextual Word Representations Using Embedding of Neighboring Entities in Knowledge Graphs0
ConcEPT: Concept-Enhanced Pre-Training for Language Models0
E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based Embeddings0
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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