Similarity-based Memory Enhanced Joint Entity and Relation Extraction
Document-level joint entity and relation extraction is a challenging information extraction problem that requires a unified approach where a single neural network performs four sub-tasks: mention detection, coreference resolution, entity classification, and relation extraction. Existing methods often utilize a sequential multi-task learning approach, in which the arbitral decomposition causes the current task to depend only on the previous one, missing the possible existence of the more complex relationships between them. In this paper, we present a multi-task learning framework with bidirectional memory-like dependency between tasks to address those drawbacks and perform the joint problem more accurately. Our empirical studies show that the proposed approach outperforms the existing methods and achieves state-of-the-art results on the BioCreative V CDR corpus.
Code (1)
Tasks
coreference-resolutionCoreference ResolutionJoint Entity and Relation ExtractionMulti-Task LearningRelationRelation ExtractionSimilar Papers 제목 키워드 기반
Knowledge Graph Enhanced Memory-Augmented Retrieval for Long Context Modeling
Long-context language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of tokens -- a challenge that semantic similarity alon…
Semantic SimilarityRelation ExtractionKnowledge GraphsEIDER: Evidence-enhanced Document-level Relation Extraction
Document-level relation extraction (DocRE) aims at extracting the semantic relations among entity pairs in a document. In DocRE, a subset of the sentences in a document, called the evidence sentences, might be sufficient…
Document-level Relation ExtractionRelationRelation ExtractionSentenceJoint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging
Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks…
graph constructionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction
Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these two tas…
Joint Entity and Relation ExtractionMulti-Task Learningnamed-entity-recognitionNamed Entity Recognition+4A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error…
Joint Entity and Relation ExtractionReading ComprehensionRelationRelation Extraction+1