Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scaling data size, we argue that diversity i…
DiversityFew-Shot LearningFew-Shot Relation ClassificationRelation+1Assessing Open-Source Large Language Models on Argumentation Mining Subtasks
We explore the capability of four open-sourcelarge language models (LLMs) in argumentation mining (AM). We conduct experiments on three different corpora; persuasive essays(PE), argumentative microtexts (AMT) Part 1 and …
ARCRelation ClassificationGDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains
Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to…
Discourse ParsingRelationRelation ClassificationLocal and Global Graph Modeling with Edge-weighted Graph Attention Network for Handwritten Mathematical Expression Recognition
In this paper, we present a novel approach to Handwritten Mathematical Expression Recognition (HMER) by leveraging graph-based modeling techniques. We introduce an End-to-end model with an Edge-weighted Graph Attention M…
ClassificationEdge ClassificationGraph AttentionRelation ClassificationWill LLMs Replace the Encoder-Only Models in Temporal Relation Classification?
The automatic detection of temporal relations among events has been mainly investigated with encoder-only models such as RoBERTa. Large Language Models (LLM) have recently shown promising performance in temporal reasonin…
In-Context LearningQuestion AnsweringRelationRelation Classification+2Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering
While being one of the most popular question types, simple questions such as "Who is the author of Cinderella?", are still not completely solved. Surprisingly, even the most powerful modern Large Language Models are pron…
Entity LinkingGraph Question AnsweringKnowledge Graphsnamed-entity-recognition+5Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest…
Feature EngineeringFew-Shot LearningFew-Shot Relation ClassificationKnowledge Graph Completion+5LLM with Relation Classifier for Document-Level Relation Extraction
Large language models (LLMs) have created a new paradigm for natural language processing. Despite their advancement, LLM-based methods still lag behind traditional approaches in document-level relation extraction (DocRE)…
Document-level Relation ExtractionRelationRelation ClassificationRelation ExtractionMultimodal Relational Triple Extraction with Query-based Entity Object Transformer
Multimodal Relation Extraction is crucial for constructing flexible and realistic knowledge graphs. Recent studies focus on extracting the relation type with entity pairs present in different modalities, such as one enti…
Knowledge GraphsObjectobject-detectionObject Detection+3A Global-Local Attention Mechanism for Relation Classification
Relation classification, a crucial component of relation extraction, involves identifying connections between two entities. Previous studies have predominantly focused on integrating the attention mechanism into relation…
ClassificationRelationRelation ClassificationRelation ExtractionImplicit Discourse Relation Classification For Nigerian Pidgin
Despite attempts to make Large Language Models multi-lingual, many of the world's languages are still severely under-resourced. This widens the performance gap between NLP and AI applications aimed at well-financed, and …
ClassificationImplicit Discourse Relation ClassificationRelationRelation ClassificationDescription Boosting for Zero-Shot Entity and Relation Classification
Zero-shot entity and relation classification models leverage available external information of unseen classes -- e.g., textual descriptions -- to annotate input text data. Thanks to the minimum data requirement, Zero-Sho…
RelationRelation ClassificationZero-Shot LearningOED: Towards One-stage End-to-End Dynamic Scene Graph Generation
Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. Conventional approaches often employ multi-stage pipelines, which typically consist of objec…
Graph Generationobject-detectionObject DetectionRelation Classification+1An Analysis of Sentential Neighbors in Implicit Discourse Relation Prediction
Discourse relation classification is an especially difficult task without explicit context markers (Prasad et al., 2008). Current approaches to implicit relation prediction solely rely on two neighboring sentences being …
PredictionRelationRelation ClassificationRelation Prediction+1KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery
This paper describes the KnowledgeHub tool, a scientific literature Information Extraction (IE) and Question Answering (QA) pipeline. This is achieved by supporting the ingestion of PDF documents that are converted to te…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+4How to Encode Domain Information in Relation Classification
Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obtain better performance is non-trivial. W…
ClassificationRelationRelation ClassificationREXEL: 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+11Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the d…
ClassificationContrastive LearningFew-Shot Relation ClassificationRelation+3Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context Information
Because protein-protein interactions (PPIs) are crucial to understand living systems, harvesting these data is essential to probe disease development and discern gene/protein functions and biological processes. Some cura…
RelationRelation ClassificationRelation ExtractionBest of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both p…
Few-Shot Relation ClassificationRelationRelation ClassificationSemantic Text Matching+1