Entity Typing
89 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
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
xwhan/Extremely-Fine-Grained-Entity-Typing
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NAACL 2019
Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.
A Systematic Study of Leveraging Subword Information for Learning Word Representations
cambridgeltl/sw_study
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NAACL 2019
The use of subword-level information (e. g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning.
Learning to Denoise Distantly-Labeled Data for Entity Typing
yasumasaonoe/DenoiseET
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NAACL 2019
In this work, we propose a two-stage procedure for handling this type of data: denoise it with a learned model, then train our final model on clean and denoised distant data with standard supervised training.
Fine-Grained Entity Typing in Hyperbolic Space
nlpAThits/figet-hyperbolic-space
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WS 2019
How can we represent hierarchical information present in large type inventories for entity typing?
KCAT: A Knowledge-Constraint Typing Annotation Tool
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision.
Zero-Shot Open Entity Typing as Type-Compatible Grounding
We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain.
Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities.
Fine-Grained Entity Typing for Domain Independent Entity Linking
For this problem, a domain is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset.
Improving Fine-grained Entity Typing with Entity Linking
HKUST-KnowComp/IFETEL
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IJCNLP 2019
Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention.
