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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 110 of 170 papers

TitleStatusHype
All Entities are Not Created Equal: Examining the Long Tail for Fine-Grained Entity Typing0
Refining Wikidata Taxonomy using Large Language ModelsCode1
GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding0
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian0
COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing0
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity LinkingCode1
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity TypingCode1
Modelling Commonsense Commonalities with Multi-Facet Concept EmbeddingsCode0
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
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