Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Human brain activity for machine attention
Cognitively inspired NLP leverages human-derived data to teach machines about language processing mechanisms. Recently, neural networks have been augmented with behavioral data to solve a range of NLP tasks spanning synt…
Dimensionality ReductionEEGElectroencephalogram (EEG)Relation ClassificationClassification de relations pour l'intelligence \'economique et concurrentielle (Relation Classification for Competitive and Economic Intelligence )
L{'}extraction de relations reliant des entit{\'e}s par des liens s{\'e}mantiques {\`a} partir de texte a fait l{'}objet de nombreux travaux visant {\`a} extraire des relations g{\'e}n{\'e}riques comme l{'}hyperonymie ou…
ClassificationGeneral ClassificationRelationRelation ClassificationA Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction
Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of these texts for the task of relation extrac…
Knowledge Graph CompletionMultiple Instance LearningRelationRelation Classification+1Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the relation classification task. Existing m…
General ClassificationMulti-Task LearningNamed Entity Recognition (NER)Relation+2MPDD: A Multi-Party Dialogue Dataset for Analysis of Emotions and Interpersonal Relationships
A dialogue dataset is an indispensable resource for building a dialogue system. Additional information like emotions and interpersonal relationships labeled on conversations enables the system to capture the emotion flow…
RelationRelation ClassificationAdapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives
BERT, a neural network-based language model pre-trained on large corpora, is a breakthrough in natural language processing, significantly outperforming previous state-of-the-art models in numerous tasks. However, there h…
General ClassificationImplicit Discourse Relation ClassificationLanguage ModelingLanguage Modelling+3On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span…
Discourse ParsingGeneral ClassificationImplicit Discourse Relation ClassificationRelation+4MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we ta…
ClassificationFew-Shot LearningFew-Shot Relation ClassificationGeneral Classification+3Relation Transformer Network
The extraction of a scene graph with objects as nodes and mutual relationships as edges is the basis for a deep understanding of image content. Despite recent advances, such as message passing and joint classification, t…
DecoderGraph GenerationRelationRelation Classification+2Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification
With the rapid growth of research publications, there is a vast amount of scholarly knowledge that needs to be organized in digital libraries. To deal with this challenge, techniques relying on knowledge-graph structures…
ClassificationGeneral ClassificationRelationRelation Classification+1SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier o…
ClusteringLanguage ModelingLanguage ModellingRelation+2Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification
Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit counterparts. We tackle situations where training data for implicit relations are lacking, and exploit dom…
ClassificationDomain AdaptationGeneral ClassificationImplicit Discourse Relation Classification+4KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification
Lexical relations describe how concepts are semantically related, in the form of relation triples. The accurate prediction of lexical relations between concepts is challenging, due to the sparsity of patterns indicating …
General ClassificationMeta-LearningRelationRelation ClassificationK-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when mul…
Dependency ParsingEntity TypingQuestion AnsweringRelation Classification+1A Neural Approach to Discourse Relation Signal Detection
Previous data-driven work investigating the types and distributions of discourse relation signals, including discourse markers such as 'however' or phrases such as 'as a result' has focused on the relative frequencies of…
RelationRelation ClassificationLearning to Relate from Captions and Bounding Boxes
In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole so…
Image CaptioningRelation ClassificationSelf-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to deno…
Entity EmbeddingsRelationRelation ClassificationRelation Extraction+2Improving Relation Classification by Entity Pair Graph
Relation classification is one of the most important tasks in the field of information extraction, and also a key component of systems that require relational understanding of unstructured text. Existing relation classif…
ClassificationGeneral ClassificationRelationRelation Classification+2KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) wi…
Entity EmbeddingsEntity TypingInductive knowledge graph completionKnowledge Graph Completion+7E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
We present a novel way of injecting factual knowledge about entities into the pretrained BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al., 2016) with BERT's native wordpiece vector s…
Entity EmbeddingsEntity LinkingLanguage ModellingQuestion Answering+1