Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Why only Micro-F1? Class Weighting of Measures for Relation Classification
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We i…
RelationRelation ClassificationFew-Shot Document-Level Relation Extraction
We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora pr…
Document-level Relation ExtractionDomain AdaptationFew-Shot LearningFew-Shot Relation Classification+4Unified Semantic Typing with Meaningful Label Inference
Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types. The inferred labels of semantic categories meaningfully inte…
Entity TypingRelation ClassificationRelation ExtractionVisually Grounded Interpretation of Noun-Noun Compounds in English
Noun-noun compounds (NNCs) occur frequently in the English language. Accurate NNC interpretation, i.e. determining the implicit relationship between the constituents of a NNC, is crucial for the advancement of many natur…
Relation ClassificationWhat do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification
Over the last five years, research on Relation Extraction (RE) witnessed extensive progress with many new dataset releases. At the same time, setup clarity has decreased, contributing to increased difficulty of reliable …
ClassificationRelationRelation ClassificationRelation ExtractionFunction-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification
The relation classification is to identify semantic relations between two entities in a given text. While existing models perform well for classifying inverse relations with large datasets, their performance is significa…
Few-Shot LearningMeta-LearningRelationRelation ClassificationERGO: Event Relational Graph Transformer for Document-level Event Causality Identification
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning without clear causal indicators. In this p…
Event Causality IdentificationNode ClassificationRelation ClassificationSentenceSelecting Optimal Context Sentences for Event-Event Relation Extraction
Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/ graphs for input documents. To achieve this goal, our work addresses the problems of subevent …
Event Relation ExtractionRelationRelation ClassificationRelation Extraction+2RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task setting of Zero-Shot Relation Triplet Extrac…
Language ModelingLanguage ModellingRelationRelation Classification+5EPPAC: Entity Pre-typing Relation Classification with Prompt AnswerCentralizing
Relation classification (RC) aims to predict the relationship between a pair of subject and object in a given context. Recently, prompt tuning approaches have achieved high performance in RC. However, existing prompt tun…
ClassificationRelationRelation ClassificationAutomatically Generating Counterfactuals for Relation Classification
The goal of relation classification (RC) is to extract the semantic relations between/among entities in the text. As a fundamental task in natural language processing, it is crucial to ensure the robustness of RC models.…
ClassificationRelationRelation ClassificationRelation ExtractionCross Domain Few-Shot Learning via Meta Adversarial Training
Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from the same domain. However, in practice, thi…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot LearningFew-Shot Relation Classification+1Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been proposed following the success of text and i…
DenoisingQuestion AnsweringRelation ClassificationTable SearchFrom Examples to Rules: Neural Guided Rule Synthesis for Information Extraction
While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, …
Enumerative SearchFew-Shot LearningProgram SynthesisRelation ClassificationPrompt-based Zero-shot Relation Classification with Semantic Knowledge Augmentation
In relation classification, recognizing unseen (new) relations for which there are no training instances is a challenging task. We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize u…
RelationRelation ClassificationSentenceZero-shot Relation ClassificationEnhanced Protein-Protein Interactions Extraction from the Literature using Entity Type- and Position-aware Representation
Since protein-protein interactions (PPIs) are crucial to understanding living systems, harvesting these data is essential to probe the development of diseases and to understand gene/protein functions and biological proce…
PositionRelationRelation ClassificationRelation ExtractionVRDFormer: End-to-End Video Visual Relation Detection With Transformers
Visual relation understanding plays an essential role for holistic video understanding. Most previous works adopt a multi-stage framework for video visual relation detection (VidVRD), which cannot capture long-term s…
ObjectRelationRelation ClassificationVideo Understanding+1Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy
Large crowd-sourced datasets are often noisy and relation classification (RC) datasets are no exception. Reannotating the entire dataset is one probable solution however it is not always viable due to time and budget con…
RelationRelation ClassificationUnified Named Entity Recognition as Word-Word Relation Classification
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest …
Chinese Named Entity RecognitionClassificationnamed-entity-recognitionNamed Entity Recognition+5Knowledge-Augmented Language Models for Cause-Effect Relation Classification
Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream tasks. In this work, we investigate the au…
Cause-Effect Relation ClassificationClassificationCommonsense Causal ReasoningLanguage Modelling+2