Entity Typing

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

8 leaderboard tables shown for this task, 8 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

12 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 92 papers with code (170 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
  2 Oct 2020 9 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer.
• ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
  8 Jul 2022 3 repositories listed
  The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
• Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
  23 Jan 2019 3 repositories listed
  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
  17 Feb 2016 3 repositories listed
  Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.
• PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning
  29 Apr 2022 2 repositories listed Syntology ran 0 of 15 samples · 15 unverified
  Meanwhile, the inference time grows log-linearly with the number of entities for all entities are traversed and compared.
• K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
  5 Feb 2020 2 repositories listed Syntology ran 7 of 14 samples · 7 unverified
  We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.
• Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
  10 Nov 2019 2 repositories listed
  LEOPARD is trained with the state-of-the-art transformer architecture and shows better generalization to tasks not seen at all during training, with as few as 4 examples per label.
• MTab: Matching Tabular Data to Knowledge Graph using Probability Models
  1 Oct 2019 2 repositories listed
  This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019).
• EntEval: A Holistic Evaluation Benchmark for Entity Representations
  31 Aug 2019 2 repositories listed
  Rich entity representations are useful for a wide class of problems involving entities.
• ERNIE: Enhanced Language Representation with Informative Entities
  17 May 2019 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP…
• Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
  13 Jul 2018 2 repositories listed
  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…
• Representation Learning of Entities and Documents from Knowledge Base Descriptions
  8 Jun 2018 2 repositories listed
  In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB).
• Refining Wikidata Taxonomy using Large Language Models
  6 Sep 2024 1 repository listed
  Due to its collaborative nature, Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxonomic paths, the presence of cycles, and…
• REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
  19 Apr 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
• The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
  12 Apr 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs.
• Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings
  25 Mar 2024 1 repository listed
  We show that this leads to embeddings which capture a more diverse range of commonsense properties, and consistently improves results in downstream tasks such as ultra-fine entity typing and ontology completion.
• Decomposed Meta-Learning for Few-Shot Sequence Labeling
  4 Mar 2024 1 repository listed
  With the detected mention spans, we further leverage the MAML-enhanced span-level prototypical network for few-shot type classification.
• Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
  23 Jan 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to…
• Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
  13 Dec 2023 1 repository listed
  However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual…
• From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
  11 Dec 2023 1 repository listed
  We can simply fine-tune the previously trained model with a small number of examples annotated under this schema.
• Calibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
  1 Nov 2023 1 repository listed
  In this paper, we present CASENT, a seq2seq model designed for ultra-fine entity typing that predicts ultra-fine types with calibrated confidence scores.
• Dense Retrieval as Indirect Supervision for Large-space Decision Making
  28 Oct 2023 1 repository listed
  Many discriminative natural language understanding (NLU) tasks have large label spaces.
• Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning
  23 Oct 2023 1 repository listed
  Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text.
• GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
  23 Oct 2023 1 repository listed Syntology ran 10 of 11 samples · 1 unverified · 11 pointer-only (licence)
  This paper introduces GeoLM, a geospatially grounded language model that enhances the understanding of geo-entities in natural language.
• Do Language Models Learn about Legal Entity Types during Pretraining?
  19 Oct 2023 1 repository listed
  Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervision for downstream tasks.
• Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs
  18 Oct 2023 1 repository listed
  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
  21 Aug 2023 1 repository listed
  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
  26 May 2023 1 repository listed
  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
  22 May 2023 1 repository listed
  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
  16 Feb 2023 1 repository listed
  Named Entity Recognition (NER) is a challenging and widely studied task that involves detecting and typing entities in text.

Syntology lines on 8 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.