Papers Zero-shot Relation Classification
“Zero-shot Relation Classification” 태그가 달린 논문 13편 · 필터 해제
GLiREL -- Generalist Model for Zero-Shot Relation Extraction
We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. Inspired by recent advancements in zero-shot na…
modelnamed-entity-recognitionNamed Entity RecognitionRelation+3On 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+2Leveraging 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+3Improving 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 ClassificationEnhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction
Zero-shot relation extraction aims to recognize (new) unseen relations that cannot be observed during training. Due to this point, recognizing unseen relations with no corresponding labeled training instances is a challe…
RelationRelation ExtractionZero-shot Relation ClassificationImproving Discriminative Learning for Zero-Shot Relation Extraction
Zero-shot relation extraction (ZSRE) aims to predict target relations that cannot be observed during training. While most previous studies have focused on fully supervised relation extraction and achieved considerably hi…
RelationRelation ExtractionSentenceZero-shot Relation ClassificationRelationPrompt: 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+5Prompt-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 ClassificationPrompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models …
ClassificationQuestion AnsweringRelationRelation Classification+6ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observe…
AttributeMulti-Task LearningRelationRelation Extraction+2Zero-shot Relation Classification from Side Information
We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data. The zero-shot learning appro…
ClassificationFew-Shot LearningRelationRelation Classification+3Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
Relation classification aims to extract semantic relations between entity pairs from the sentences. However, most existing methods can only identify seen relation classes that occurred during training. To recognize unsee…
ClassificationDescriptiveGeneral ClassificationKnowledge Graph Embeddings+6Zero-shot Relation Classification as Textual Entailment
We consider the task of relation classification, and pose this task as one of textual entailment. We show that this formulation leads to several advantages, including the ability to (i) perform zero-shot relation classif…
ClassificationGeneral ClassificationKnowledge Base PopulationNatural Language Inference+7