Entity Typing
88 papers with code • 8 benchmarks • 12 datasets
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
Benchmarks
These leaderboards are used to track progress in Entity Typing
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Libraries
Use these libraries to find Entity Typing models and implementations
OntoNotes 5.0
FIGER
Few-NERD
AIDA CoNLL-YAGO
Open Entity
PreCo
GUM
Most implemented papers
PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning
alipay/parameter_inference_efficient_pie
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Meanwhile, the inference time grows log-linearly with the number of entities for all entities are traversed and compared.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
amazon-research/ReFinED
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NAACL (ACL) 2022
The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs.
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
We present BPEmb, a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE).
Fine-grained Entity Typing through Increased Discourse Context and Adaptive Classification Thresholds
sheng-z/figet
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SEMEVAL 2018
Fine-grained entity typing is the task of assigning fine-grained semantic types to entity mentions.
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings.
Ultra-Fine Entity Typing
We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e. g. skyscraper, songwriter, or criminal) that describe appropriate types for the target entity.
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases
Fine-grained entity typing aims at identifying the semantic type of an entity in KB.
Put It Back: Entity Typing with Language Model Enhancement
Entity typing aims to classify semantic types of an entity mention in a specific context.
