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In this paper, we propose a novel method for joint entity and relation extraction from unstructured text by framing it as a conditional sequence generation problem.
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Multi-Task Identification of Entities, Relations, and Coreferencefor Scientific Knowledge Graph Construction
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Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations
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LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model
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A sequence-to-sequence approach for document-level relation extraction
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GenIE: Generative Information Extraction
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Characterizing and addressing the issue of oversmoothing in neural autoregressive sequence modeling
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Joint Entity and Relation Extraction with Set Prediction Networks
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Gradient-based Constrained Sampling from Language Models
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Autoregressive Structured Prediction with Language Models
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Unified Structure Generation for Universal Information Extraction
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Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural Networks
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Generative Knowledge Graph Construction: A Review
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Structured Voronoi Sampling
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Efficient Guided Generation for Large Language Models
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Large Language Models for Generative Information Extraction: A Survey
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Filtered Semi-Markov CRF
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