Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
The superior performance of supervised classification methods in the information extraction (IE) area heavily relies on a large amount of gold standard data. Recent zero-shot classification methods converted the task to …
ClassificationNatural Language InferenceRelation Classificationzero-shot-classification+2Prompting Implicit Discourse Relation Annotation
Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowdsourcing workers. Nonetheless, ChatGPT's…
ClassificationImplicit Discourse Relation ClassificationMultiple-choicePrompt Engineering+2Graph Language Models
While Language Models (LMs) are the workhorses of NLP, their interplay with structured knowledge graphs (KGs) is still actively researched. Current methods for encoding such graphs typically either (i) linearize them for…
Knowledge GraphsLanguage ModelingLanguage ModellingRelation ClassificationNoise in Relation Classification Dataset TACRED: Characterization and Reduction
The overarching objective of this paper is two-fold. First, to explore model-based approaches to characterize the primary cause of the noise. in the RE dataset TACRED Second, to identify the potentially noisy instances. …
ClassificationRelationRelation ClassificationImproving Vision-and-Language Reasoning via Spatial Relations Modeling
Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, large-scale pre-training approaches have …
Position regressionRelationRelation ClassificationVisual Commonsense Reasoning+1Discourse Relations Classification and Cross-Framework Discourse Relation Classification Through the Lens of Cognitive Dimensions: An Empirical Investigation
Existing discourse formalisms use different taxonomies of discourse relations, which require expert knowledge to understand, posing a challenge for annotation and automatic classification. We show that discourse relation…
ClassificationDiscourse SegmentationRelation ClassificationDynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning
Temporal relation classification is a pair-wise task for identifying the relation of a temporal link (TLINK) between two mentions, i.e. event, time, and document creation time (DCT). It leads to two crucial limits: 1) Tw…
Multi-Task LearningRelationRelation ClassificationTemporal Relation Classification+1CreoleVal: Multilingual Multitask Benchmarks for Creoles
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.While the genealogical ties between Creoles and a number of highly-resourced languages imply a signif…
Machine TranslationReading ComprehensionRelation ClassificationTransfer LearningA Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods
Named Entity Recognition (NER) and Relation Classification (RC) are important steps in extracting information from unstructured text and formatting it into a machine-readable format. We present a survey of recent deep le…
ClassificationFew-Shot Learninggraph constructionInformation Retrieval+9CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted …
Domain AdaptationFew-Shot Relation ClassificationRelationRelation Classification+1Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research. Despite the growing interest, a critical gap persists in u…
ClassificationNatural Language UnderstandingRelationRelation Classification+2PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the …
Document-level Relation ExtractionRelationRelation ClassificationRelation Extraction+1Rationale-Enhanced Language Models are Better Continual Relation Learners
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the mo…
Continual Relation ExtractionRelationRelation ClassificationRelation ExtractionMultiple Relations Classification using Imbalanced Predictions Adaptation
The relation classification task assigns the proper semantic relation to a pair of subject and object entities; the task plays a crucial role in various text mining applications, such as knowledge graph construction and …
Classificationgraph constructionRelationRelation Classification+2Advancing Relation Extraction through Language Probing with Exemplars from Set Co-Expansion
Relation Extraction (RE) is a pivotal task in automatically extracting structured information from unstructured text. In this paper, we present a multi-faceted approach that integrates representative examples and through…
ClassificationRelationRelation ClassificationRelation ExtractionLeveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification
Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use expert knowledge from existing annotation…
ClassificationNatural Language InferenceRelationRelation Classification+3Explaining Relation Classification Models with Semantic Extents
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art syst…
ClassificationRelationRelation ClassificationImproving Zero-shot Relation Classification via Automatically-acquired Entailment Templates
While fully supervised relation classification (RC) models perform well on large-scale datasets, their performance drops drastically in low-resource settings. As generating annotated examples are expensive, recent zero-s…
Natural Language InferenceRelationRelation ClassificationZero-shot Relation ClassificationA Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification
Though discourse parsing can help multiple NLP fields, there has been no wide language model search done on implicit discourse relation classification. This hinders researchers from fully utilizing public-available model…
Discourse ParsingImplicit Discourse Relation ClassificationLanguage ModelingLanguage Modelling+3Mutually Guided Few-shot Learning for Relational Triple Extraction
Knowledge graphs (KGs), containing many entity-relation-entity triples, provide rich information for downstream applications. Although extracting triples from unstructured texts has been widely explored, most of them req…
Cross-Domain Few-ShotDecoderFew-Shot LearningKnowledge Graphs+2