Papers Entity Typing
“Entity Typing” 태그가 달린 논문 175편 · 필터 해제
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 KnowledgeRefining Wikidata Taxonomy using Large Language Models
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 t…
Entity TypingGraph MiningGeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding
In human reading and communication, individuals tend to engage in geospatial reasoning, which involves recognizing geographic entities and making informed inferences about their interrelationships. To mimic such cognitiv…
Entity TypingLanguage ModelingLanguage ModellingNatural Language Understanding+1Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian
Addressing the challenge of limited annotated data in specialized fields and low-resource languages is crucial for the effective use of Language Models (LMs). While most Large Language Models (LLMs) are trained on genera…
Document ClassificationEntity TypingGeneral Knowledgezero-shot-classification+1COTET: Cross-view Optimal Transport for Knowledge Graph Entity Typing
Knowledge graph entity typing (KGET) aims to infer missing entity type instances in knowledge graphs. Previous research has predominantly centered around leveraging contextual information associated with entities, which …
Entity TypingKnowledge GraphsREXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer …
Benchmarkingcoreference-resolutionCoreference ResolutionDocument-level Closed Information Extraction+11The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the \textit{\textbf{structural knowledge}} in the local neighborhood of e…
Entity TypingKnowledge GraphsRe-RankingModelling Commonsense Commonalities with Multi-Facet Concept Embeddings
Concept embeddings offer a practical and efficient mechanism for injecting commonsense knowledge into downstream tasks. Their core purpose is often not to predict the commonsense properties of concepts themselves, but ra…
Entity TypingFrom Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
User alignment is crucial for adapting general-purpose language models (LMs) to downstream tasks, but human annotations are often not available for all types of instructions, especially those with customized constraints.…
Abstractive Text SummarizationEntity TypingLanguage ModelingLanguage Modelling+1Decomposed Meta-Learning for Few-Shot Sequence Labeling
Few-shot sequence labeling is a general problem formulation for many natural language understanding tasks in data-scarcity scenarios, which require models to generalize to new types via only a few labeled examples. Recen…
Entity TypingEvent DetectionFew-shot NERMeta-Learning+8Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nev…
Entity TypingNatural Language InferenceConcEPT: Concept-Enhanced Pre-Training for Language Models
Pre-trained language models (PLMs) have been prevailing in state-of-the-art methods for natural language processing, and knowledge-enhanced PLMs are further proposed to promote model performance in knowledge-intensive ta…
Entity LinkingEntity TypingRobust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
Few-shot named entity recognition (NER) aims to recognize novel named entities in low-resource domains utilizing existing knowledge. However, the present few-shot NER models assume that the labeled data are all clean wit…
Adversarial AttackEntity Typingfew-shot-nerFew-shot NER+5From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
For the task of fine-grained entity typing (FET), due to the use of a large number of entity types, it is usually considered too costly to manually annotating a training dataset that contains an ample number of examples …
Entity TypingCalibrated Seq2seq Models for Efficient and Generalizable Ultra-fine Entity Typing
Ultra-fine entity typing plays a crucial role in information extraction by predicting fine-grained semantic types for entity mentions in text. However, this task poses significant challenges due to the massive number of …
Entity Typing