Entity Typing
56 papers with code • 6 benchmarks • 7 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
FIGER
OntoNotes 5.0
Open Entity
Few-NERD
PreCo
GUM
Most implemented papers
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
studio-ousia/luke
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EMNLP 2020
In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
roomylee/entity-aware-relation-classification
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Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.
Representation Learning of Entities and Documents from Knowledge Base Descriptions
In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB).
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies.
EntEval: A Holistic Evaluation Benchmark for Entity Representations
ZeweiChu/EntEval
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IJCNLP 2019
Rich entity representations are useful for a wide class of problems involving entities.
MTab: Matching Tabular Data to Knowledge Graph using Probability Models
This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019).
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.
