Papers Relation Classification
“Relation Classification” 태그가 달린 논문 476편 · 필터 해제
Biomedical Relation Classification by single and multiple source domain adaptation
Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it…
ClassificationDomain AdaptationGeneral ClassificationRelation+1Representing Movie Characters in Dialogues
We introduce a new embedding model to represent movie characters and their interactions in a dialogue by encoding in the same representation the language used by these characters as well as information about the other pa…
Question AnsweringRelation ClassificationVisual Question AnsweringVisual Question Answering (VQA)Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation
Words in different languages rarely cover the exact same semantic space. This work characterizes differences in meaning between words across languages using semantic relations that have been used to relate the meaning of…
ClassificationCross-Lingual TransferGeneral ClassificationKnowledge Distillation+3Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains
Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to so…
Discourse ParsingGeneral ClassificationImplicit Discourse Relation ClassificationRelation+2Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations. Modeling the …
ClassificationDiscourse ParsingGeneral ClassificationImplicit Discourse Relation Classification+2FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-a…
ClassificationDomain AdaptationFew-Shot Relation ClassificationGeneral Classification+2Type-aware Convolutional Neural Networks for Slot Filling
The slot filling task aims at extracting answers for queries about entities from text, such as "Who founded Apple". In this paper, we focus on the relation classification component of a slot filling system. We propose ty…
coreference-resolutionCoreference ResolutionGeneral ClassificationRelation+5Argumentative Relation Classification as Plausibility Ranking
We formulate argumentative relation classification (support vs. attack) as a text-plausibility ranking task. To this aim, we propose a simple reconstruction trick which enables us to build minimal pairs of plausible and …
ClassificationGeneral ClassificationRelationRelation ClassificationSpan-based Joint Entity and Relation Extraction with Transformer Pre-training
We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our key contribution is a light-weight reasoning on BERT embeddings, which features entity recognition and filtering, as well as…
Joint Entity and Relation ExtractionNamed Entity Recognition (NER)RelationRelation Classification+2Knowledge Enhanced Contextual Word Representations
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a g…
Entity LinkingEntity TypingLanguage ModelingLanguage Modelling+3Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications. These models, however, retain some of the limitations of traditional static word embeddings. In particular, they …
Language ModelingLanguage ModellingLexical SimplificationMasked Language Modeling+6Zero-shot transfer for implicit discourse relation classification
Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated …
ClassificationGeneral ClassificationImplicit Discourse Relation ClassificationRelation+2TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events
Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs. This restricts the ability of sys…
ArticlesRelation ClassificationSentenceTemporal Relation ClassificationWhich aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification
Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use. We propose to decompose the task into smaller, mostly binar…
General ClassificationRelationRelation ClassificationRelation PredictionDiscourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks
Word pairs across argument spans have been shown to be effective for predicting the discourse relation between them. We propose an approach to distill knowledge from word pairs for discourse relation classification with …
Binary ClassificationClassificationGeneral ClassificationPrediction+3EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks
Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations a…
General ClassificationKnowledge GraphsNatural Language InferenceQuestion Answering+3Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text
Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the bes…
Joint Entity and Relation ExtractionLanguage ModelingLanguage Modellingnamed-entity-recognition+5Few-shot Text Classification with Distributional Signatures
In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks. However, directly …
ClassificationFew-Shot Text ClassificationGeneral ClassificationMeta-Learning+3Zero-shot transfer for implicit discourse relation classification
Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more challenging by the fact that annotated …
ClassificationGeneral ClassificationImplicit Discourse Relation ClassificationRelation+2SpanBERT: Improving Pre-training by Representing and Predicting Spans
We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the…
Coreference ResolutionLinguistic AcceptabilityNatural Language InferenceOpen-Domain Question Answering+6