Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Adversarial Feature Adaptation for Cross-lingual Relation Classification
Relation Classification aims to classify the semantic relationship between two marked entities in a given sentence. It plays a vital role in a variety of natural language processing applications. Most existing methods fo…
ClassificationDomain AdaptationGeneral ClassificationGenerative Adversarial Network+6A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification
Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak context…
General ClassificationImplicit Discourse Relation ClassificationMachine TranslationRelation+3Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences
Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence…
ClassificationGeneral ClassificationRelationRelation Classification+1Neural Relation Classification with Text Descriptions
Relation classification is an important task in natural language processing fields. State-of-the-art methods usually concentrate on building deep neural networks based classification models on the training data in which …
ClassificationGeneral ClassificationRelationRelation Classification+1Exploratory Neural Relation Classification for Domain Knowledge Acquisition
The state-of-the-art methods for relation classification are primarily based on deep neural net- works. This kind of supervised learning method suffers from not only limited training data, but also the large number of lo…
ClassificationGeneral ClassificationKnowledge GraphsRelation+1Convolutional Gated Recurrent Units for Medical Relation Classification
Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification. We propose a unified architecture, which exploits the advantages of CNN and RNN…
ClassificationGeneral ClassificationRelationRelation ClassificationSemantic Relation Classification: Task Formalisation and Refinement
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, se…
ClassificationGeneral ClassificationRelationRelation ClassificationOlive Oil is Made \textitof Olives, Baby Oil is Made \textitfor Babies: Interpreting Noun Compounds Using Paraphrases in a Neural Model
Automatic interpretation of the relation between the constituents of a noun compound, e.g. olive oil (source) and baby oil (purpose) is an important task for many NLP applications. Recent approaches are typically based o…
MemorizationRelationRelation ClassificationSemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers
This paper describes the first task on semantic relation extraction and classification in scientific paper abstracts at SemEval 2018. The challenge focuses on domain-specific semantic relations and includes three differe…
ClassificationGeneral ClassificationKeyword ExtractionRelation+2LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification
We present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-internal, word-level features and external k…
Feature EngineeringGeneral ClassificationRelationRelation Classification+2The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings
This paper describes our submission for SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. Our model is based on the end-to-end relation extraction model of (Miwa and…
ClassificationGeneral ClassificationRelationRelation Classification+2UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network
This paper reports our participation for SemEval-2018 Task 7 on extraction and classification of relationships between entities in scientific papers. Our approach is based on the use of a Convolutional Neural Network (CN…
ClusteringGeneral ClassificationRelationRelation Classification+3MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network
SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of models: Linear classifiers, Long Short-Term Memory (L…
ArticlesCommon Sense ReasoningData AugmentationGeneral Classification+5IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification
This paper presents our participation for sub-task1 (1.1 and 1.2) in SemEval 2018 task 7: Semantic Relation Extraction and Classification in Scientific Papers (G{\'a}bor et al., 2018). We experimented on this task with t…
General ClassificationNamed Entity Recognition (NER)RelationRelation Classification+2Texterra at SemEval-2018 Task 7: Exploiting Syntactic Information for Relation Extraction and Classification in Scientific Papers
In this work we evaluate applicability of entity pair models and neural network architectures for relation extraction and classification in scientific papers at SemEval-2018. We carry out experiments with representing en…
ClassificationGeneral ClassificationRelation ClassificationRelation Extraction+1UniMa at SemEval-2018 Task 7: Semantic Relation Extraction and Classification from Scientific Publications
Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers. This problem is addressed by the SemEval 2018 Task 7 on extracting and classifying rela…
General ClassificationRelationRelation ClassificationRelation ExtractionClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings
In this paper we describe our system for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). We compare two models for classification…
ClassificationGeneral ClassificationQuestion AnsweringRelation Classification+3TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts
We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model. Both models combine dense p…
General ClassificationRelationRelation ClassificationRelation ExtractionSciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification
This paper describes our system, SciREL (Scientific abstract RELation extraction system), developed for the SemEval 2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers. We present a feature-…
Feature EngineeringGeneral ClassificationRelationRelation Classification+1NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers
The paper presents NTNU{'}s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid searc…
Classificationfeature selectionGeneral ClassificationRelation+2