Entity Typing
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Benchmarks
Open Entity
Ontonotes v5 (English)
Open Entity
AIDA-CoNLL
DocRED-IE
FIGER
Freebase FIGER
OntoNotes
Most implemented
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Papers
Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach
Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts. Recently, large language models (LLMs) have demonstrated the capacity to achieve …
Information ExtractionMulti-Task LearningEntity TypingNarrative-UFET: Narrative Generation for Ultra-Fine Entity Typing
Ultra-fine entity typing (UFET) assigns highly specific types to entity mentions, but current approaches struggle with types in the long tail. We hypothesize that a key limitation is the reliance on sentence-level contex…
Entity TypingSAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction
Multimodal IE in social media is difficult because a post may attach multiple images that are weakly related, redundant, or even misleading with respect to the text. In this setting, always-on multimodal fusion wastes co…
Relation ClassificationInformation ExtractionRelation ExtractionEntity TypingMHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models
Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they apply it to clinically salient structured ju…
Entity TypingPASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelines
Modern NLP and LLM systems are pipelines: named entity recognition (NER) -> entity disambiguation (NED) -> entity typing, retrieval-augmented generation (retriever -> reader), and agentic chains of planner -> tool -> cri…
Entity DisambiguationEntity TypingAll Entities are Not Created Equal: Examining the Long Tail for Fine-Grained Entity Typing
Pre-trained language models (PLMs) are trained on large amounts of data, which helps capture world knowledge alongside linguistic competence. Due to this, they are extensively used for ultra-fine entity typing tasks, whe…
AllEntity TypingWorld Knowledge