Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)
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| Challenge: | Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity. |
| Approach: | They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach. |
| Outcome: | The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties. |
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| Challenge: | Existing models for extracting symptoms from clinical conversations are inherently difficult. |
| Approach: | They propose two new deep learning models tailored for a new application . they propose a hierarchical span-attribute tagging model and a sequence-to-sequence model . |
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Span-Level Model for Relation Extraction (P19-1)
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| Challenge: | Recent approaches for this span-level task have inherent limitations. |
| Approach: | They propose a model which directly models all possible spans and performs joint entity mention detection and relation extraction. |
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Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations (2020.coling-main)
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| Challenge: | Existing methods treat each span token equally important, ignoring significant features. |
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Pre-training Entity Relation Encoder with Intra-span and Inter-span Information (2020.emnlp-main)
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| Challenge: | Existing pre-trained models do not handle text spans and relation among text span pairs. |
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Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling (P18-2)
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Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction (2024.findings-emnlp)
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
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A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
| Approach: | They propose a table-to-graph generation model for joint extraction of entities and relations at document-level. |
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An Improved Baseline for Sentence-level Relation Extraction (2022.aacl-short)
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| Challenge: | Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. |
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Relation Extraction using Explicit Context Conditioning (N19-1)
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| Challenge: | Existing methods for relation extraction fail to capture complex and long dependencies . end-to-end models that learn both NER and RE can solve this problem . |
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Entity, Relation, and Event Extraction with Contextualized Span Representations (D19-1)
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| Challenge: | Existing frameworks for named entity recognition, relation extraction, and event extraction can be easily adapted for new tasks or datasets. |
| Approach: | They propose a framework that enumerates, refins, and scores text spans to capture local (within-sentence) and global (cross-sentent) context. |
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