Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved im…
RelationRelation ClassificationSentenceTripletCogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification
We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model. In spite of several…
Hypernym DiscoveryLanguage ModelingLanguage ModellingMultilingual text classification+3DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understan…
Dialog Relation ExtractionGeneral ClassificationRelationRelation Classification+1ACNLP at SemEval-2020 Task 6: A Supervised Approach for Definition Extraction
We describe our contribution to two of the subtasks of SemEval 2020 Task 6, DeftEval: Extracting term-definition pairs in free text. The system for Subtask 1: Sentence Classification is based on a transformer architectur…
ClassificationDefinition ExtractionRelation ClassificationSentence+2Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction
This paper describes our approach to {``}DeftEval: Extracting Definitions from Free Text in Textbooks{''} competition held as a part of Semeval 2020. The task was devoted to finding and labeling definitions in texts. Def…
ClassificationDefinition ExtractionMulti-Task Learningnamed-entity-recognition+5Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification
We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments. The attention-worthy words in arguments are crucial clues for classifying the discourse …
Implicit Discourse Relation ClassificationRelationRelation ClassificationMeta-Information Guided Meta-Learning for Few-Shot Relation Classification
Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limit…
ClassificationFew-Shot Relation ClassificationMeta-LearningRelation+1A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification
Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emerge…
Few-Shot LearningFew-Shot Relation ClassificationLifelong learningRelation+2RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER
Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. Most of the multimodal methods used attention mechanisms to capture the text-related visual information. However, unrelate…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+2Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network
In this paper, we propose a new few-shot text classification method. Compared with supervised learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled …
Few-Shot Text ClassificationGraph Neural NetworkLanguage ModelingLanguage Modelling+4CollFrEn: Rich Bilingual English–French Collocation Resource
Collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words traditionally pose a challenge to language learners and many Natural Language Processing (NLP) applications alike. Reliab…
Machine TranslationRelation ClassificationText GenerationTranslation+1Joint Extraction of Entity and Relation with Information Redundancy Elimination
To solve the problem of redundant information and overlapping relations of the entity and relation extraction model, we propose a joint extraction model. This model can directly extract multiple pairs of related entities…
DecoderLanguage ModelingLanguage Modellingnamed-entity-recognition+5Entity Recognition and Relation Extraction from Scientific and Technical Texts in Russian
This paper is devoted to the study of methods for information extraction (entity recognition and relation classification) from scientific texts on information technology. Scientific publications provide valuable informat…
Keyword ExtractionRelationRelation ClassificationRelation ExtractionZero-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+3CODER: Knowledge infused cross-lingual medical term embedding for term normalization
This paper proposes CODER: contrastive learning on knowledge graphs for cross-lingual medical term representation. CODER is designed for medical term normalization by providing close vector representations for different …
Contrastive LearningKnowledge GraphsRelationRelation Classification+3Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism
With the explosive growth of biomedical literature, designing automatic tools to extract information from the literature has great significance in biomedical research. Recently, transformer-based BERT models adapted to t…
RelationRelation ClassificationRelation ExtractionWithin-Between Lexical Relation Classification
We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words. Our model integrates relational and distributional signals, forming an effective sub-space representat…
ClassificationGeneral ClassificationRelationRelation ClassificationAPE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning
Peer review and rebuttal, with rich interactions and argumentative discussions in between, are naturally a good resource to mine arguments. However, few works study both of them simultaneously. In this paper, we introduc…
Argument Pair Extraction (APE)Multi-Task LearningRelation ClassificationThe 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…
ClassificationQuestion AnsweringRelationRelation ClassificationRelative and Incomplete Time Expression Anchoring for Clinical Text
Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing cha…
ClassificationRelation Classification