Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Jointly Extracting Multiple Triplets with Multilayer Translation Constraints
Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus. Conventional extraction models use a pipeline of named e…
Knowledge Base Constructionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Joint Type Inference on Entities and Relations via Graph Convolutional Networks
We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, w…
RelationRelation ClassificationRelation ExtractionVocal Bursts Type PredictionARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification
Distant supervision is widely used in relation classification in order to create large-scale training data by aligning a knowledge base with an unlabeled corpus. However, it also introduces amounts of noisy labels where …
ClassificationGeneral ClassificationRelationRelation Classification+1Collocation Classification with Unsupervised Relation Vectors
Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embed…
ClassificationGeneral ClassificationRelationRelation Classification+1Model-Agnostic Meta-Learning for Relation Classification with Limited Supervision
In this paper we frame the task of supervised relation classification as an instance of meta-learning. We propose a model-agnostic meta-learning protocol for training relation classifiers to achieve enhanced predictive p…
ClassificationGeneral ClassificationMeta-LearningRelation+1Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data
In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances…
Multi-Task Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+6Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate the noise by multi-instance learning and …
Common Sense ReasoningLanguage ModellingRelationRelation Classification+1Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query…
Few-Shot Relation ClassificationGeneral ClassificationRelationRelation ClassificationMatching the Blanks: Distributional Similarity for Relation Learning
General purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their su…
Few-Shot Relation ClassificationRelationRelation ClassificationRelation ExtractionRelation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNN…
General ClassificationRelationRelation ClassificationExploiting Noisy Data in Distant Supervision Relation Classification
Distant supervision has obtained great progress on relation classification task. However, it still suffers from noisy labeling problem. Different from previous works that underutilize noisy data which inherently characte…
ClassificationGeneral Classificationreinforcement-learningReinforcement Learning+3Towards the Data-driven System for Rhetorical Parsing of Russian Texts
Results of the first experimental evaluation of machine learning models trained on Ru-RSTreebank {--} first Russian corpus annotated within RST framework {--} are presented. Various lexical, quantitative, morphological, …
BIG-bench Machine LearningClassificationGeneral ClassificationRelation+1Neural-Symbolic Argumentation Mining: an Argument in Favor of Deep Learning and Reasoning
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit…
Argument MiningComponent ClassificationLink PredictionPosition+2Enriching Pre-trained Language Model with Entity Information for Relation Classification
Relation classification is an important NLP task to extract relations between entities. The state-of-the-art methods for relation classification are primarily based on Convolutional or Recurrent Neural Networks. Recently…
General ClassificationLanguage ModelingLanguage ModellingRelation+3ERNIE: Enhanced Language Representation with Informative Entities
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP task…
Entity LinkingEntity TypingKnowledge GraphsLinguistic Acceptability+6Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting
Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation class…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Advancing NLP with Cognitive Language Processing Signals
When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive language processing data such as eye-tra…
EEGElectroencephalogram (EEG)General Classificationnamed-entity-recognition+4Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters
The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks. In literature analysis, such networks have mostly been analyzed without particular r…
RelationRelation ClassificationMining Discourse Markers for Unsupervised Sentence Representation Learning
Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data -- such as discourse markers between sentences …
Relation ClassificationRepresentation LearningSentenceSentence EmbeddingsA Single Attention-Based Combination of CNN and RNN for Relation Classification
As a vital task in natural language processing, relation classification aims to identify relation types between entities from texts. In this paper, we propose a novel Att-RCNN model to extract text features and classify …
General ClassificationRelationRelation ClassificationSentence