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
Representation Learning of Entities and Documents from Knowledge Base DescriptionsCode1
Transforming Wikipedia into a Large-Scale Fine-Grained Entity Type Corpus0
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification ThresholdsCode0
Finer Grained Entity Typing with TypeNet0
Path-Based Attention Neural Model for Fine-Grained Entity Typing0
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 LanguagesCode0
Corpus-level Fine-grained Entity Typing0
Fine-Grained Entity Typing with High-Multiplicity Assignments0
Nonsymbolic Text Representation0
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities0
Noise Mitigation for Neural Entity Typing and Relation Extraction0
Label Embedding for Zero-shot Fine-grained Named Entity Typing0
AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label EmbeddingCode1
Multi-source named entity typing for social media0
Corpus-level Fine-grained Entity Typing Using Contextual Information0
Building a Fine-Grained Entity Typing System Overnight for a New X (X = Language, Domain, Genre)0
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label EmbeddingCode1
FINET: Context-Aware Fine-Grained Named Entity Typing0
SinoCoreferencer: An End-to-End Chinese Event Coreference Resolver0
Automatic lexical semantic classification of nouns0
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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