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
Latest papers
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs.
SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding
However, LLMs are sometimes too footloose for natural language understanding (NLU) tasks which always have restricted output and input format.
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks.
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
In this paper, we propose to improve on this process by pre-training an entity encoder such that embeddings of coreferring entities are more similar to each other than to the embeddings of other entities.
Dynamic Named Entity Recognition
kawatami/dner
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Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text.
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing
We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing.
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field
We use mean-field variational inference for efficient type inference on very large type sets and unfold it as a neural network module to enable end-to-end training.
Learning to Select from Multiple Options
jiangshdd/learningtoselect
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To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
yibowang214/ctm
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In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China.
