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 with no code
UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models
In this paper, we propose a UNified knowledge inTERface, UNTER, to provide a unified perspective to exploit both structured knowledge and unstructured knowledge.
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Basically, an instance-dependent soft prefix, derived from fact-counterfactual pairs in the label space, is leveraged to complement the language verbalizers in many-class classification.
SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation
Characterizing geo-entities is integral to various application domains, such as geo-intelligence and map comprehension, while a key challenge is to capture the spatial-varying context of an entity.
Denoising Enhanced Distantly Supervised Ultrafine Entity Typing
Specifically, we build a noise model to estimate the unknown labeling noise distribution over input contexts and noisy type labels.
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity.
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Entity typing is the task of assigning or inferring the semantic type of an entity in a KG.
MLRIP: Pre-training a military language representation model with informative factual knowledge and professional knowledge base
Incorporating prior knowledge into pre-trained language models has proven to be effective for knowledge-driven NLP tasks, such as entity typing and relation extraction.
Conditional set generation using Seq2seq models
Conditional set generation learns a mapping from an input sequence of tokens to a set.
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e. g., M-BERT).
Prototypical Verbalizer for Prompt-based Few-shot Tuning
However, manual verbalizers heavily depend on domain-specific prior knowledge and human efforts, while finding appropriate label words automatically still remains challenging. In this work, we propose the prototypical verbalizer (ProtoVerb) which is built directly from training data.
