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

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

Content

Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Representation Learning of Entities and Documents from Knowledge Base Descriptions

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

ERNIE: Enhanced Language Representation with Informative Entities

EntEval: A Holistic Evaluation Benchmark for Entity Representations

MTab: Matching Tabular Data to Knowledge Graph using Probability Models

Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters