Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Logic-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+6RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational Searching
Distant supervision (DS) aims to generate large-scale heuristic labeling corpus, which is widely used for neural relation extraction currently. However, it heavily suffers from noisy labeling and long-tail distributions …
Denoisingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+3Meta-Learning for Neural Relation Classification with Distant Supervision
Distant supervision provides a means to create a large number of weakly labeled data at low cost for relation classification. However, the resulting labeled instances are very noisy, containing data with wrong labels. Ma…
ClassificationGeneral ClassificationMeta-LearningRelation+1Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, …
Few-Shot Relation ClassificationRelationRelation ClassificationSentenceThe RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification
Relation classification is one of the key topics in information extraction, which can be used to construct knowledge bases or to provide useful information for question answering. Current approaches for relation classifi…
ClassificationGeneral ClassificationQuestion AnsweringRelation+1TDRE: A Tensor Decomposition Based Approach for Relation Extraction
Extracting entity pairs along with relation types from unstructured texts is a fundamental subtask of information extraction. Most existing joint models rely on fine-grained labeling scheme or focus on shared embedding p…
RelationRelation ClassificationRelation ExtractionSentence+2Relation Classification as Two-way Span-Prediction
The current supervised relation classification (RC) task uses a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat the RC task as span-prediction (SP) probl…
ClassificationGeneral ClassificationPredictionQuestion Answering+4Semi-supervised Relation Extraction via Incremental Meta Self-Training
To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods s…
Meta-LearningPseudo LabelRelationRelation Classification+1LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The pr…
Common Sense ReasoningEntity TypingExtractive Question-AnsweringLanguage Modeling+6小样本关系分类研究综述(Few-Shot Relation Classification: A Survey)
关系分类作为构建结构化知识的重要一环,在自然语言处理领域备受关注。但在很多应用领域中(医疗、金融领域),收集充足的用于训练关系分类模型的数据是十分困难的。近年来,仅需要少量训练样本的小样本学习研究逐渐新兴于各大领域。本文对近期小样本关系分类模型与方法进行了系统的综述。根据度量方法的不同,将现有方法分为原型式和分布式两大类。根据是否利用额外信息,将模型分为预训练和非预训练两大类。此外,除了常规设定下的小样本学习,本文还梳理了跨领域和稀缺资…
Few-Shot Relation ClassificationRelation ClassificationEntity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction
Joint entity and relation extraction has received increasing interests recently, due to the capability of utilizing the interactions between both steps. Among existing studies, the Multi-Head Selection (MHS) framework is…
Joint Entity and Relation ExtractionLanguage ModelingLanguage ModellingPosition+3AutoRC: Improving BERT Based Relation Classification Models via Architecture Search
Although BERT based relation classification (RC) models have achieved significant improvements over the traditional deep learning models, it seems that no consensus can be reached on what is the optimal architecture. Fir…
General ClassificationNeural Architecture SearchRelationRelation ClassificationThe Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis
Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase, thereby leading to the deterioration of the tail performance. Existing solutions usually address this issu…
General ClassificationRelationRelation ClassificationSemantic Relations and Deep Learning
The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid \'O S\'eaghdha has been published in April 2021 by Morgan & Claypool (www.morganclaypoolpublishers.…
Deep LearningRelation ClassificationUPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of gran…
Definition ExtractionGeneral ClassificationLanguage ModelingLanguage Modelling+1HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation
Scene graph generation aims to produce structured representations for images, which requires to understand the relations between objects. Due to the continuous nature of deep neural networks, the prediction of scene grap…
General ClassificationGraph GenerationKnowledge Graphsobject-detection+3Implicit Discourse Relation Classification: We Need to Talk about Evaluation
Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation …
ClassificationGeneral ClassificationImplicit Discourse Relation ClassificationRelation+2A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through…
Multi-Task LearningRelationRelation ClassificationRelation Extraction+1Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical fo…
Joint Entity and Relation ExtractionRelationRelation ClassificationRelation ExtractionNLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing Literature
We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various informat…
ArticlesMachine Translationnamed-entity-recognitionNamed Entity Recognition+6