| Challenge: | Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction. |
| Approach: | They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion. |
| Outcome: | The proposed convolutional network performs better than baseline models on multiple datasets. |
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| Challenge: | Existing convolutional neural networks (CNNs) use sparse representations of text, such as bag-of-words. |
| Approach: | They propose an adaptive convolution for text classification to give flexibility to convolutional neural networks (CNNs) they attach filter-generating networks to convevolution blocks in existing CNNs . |
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Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
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| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
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Joint Type Inference on Entities and Relations via Graph Convolutional Networks (P19-1)
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| Challenge: | a novel graph convolutional network (GCN) is proposed for the task of joint entity relation extraction. |
| Approach: | They propose a graph convolutional network running on an entity-relation bipartite graph . they propose combining two different methods to perform joint entity relation extraction . |
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Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
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Graph Convolution over Pruned Dependency Trees Improves Relation Extraction (D18-1)
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| Challenge: | Existing dependency-based models neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively. |
| Approach: | They propose an extension of graph convolutional networks that is tailored for relation extraction by pruning dependency trees too aggressively. |
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A Frustratingly Easy Approach for Entity and Relation Extraction (2021.naacl-main)
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
| Approach: | They propose a pipelined approach for entity and relation extraction that uses two independent encoders to construct the relation model. |
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Using active learning to expand training data for implicit discourse relation recognition (D18-1)
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| Challenge: | Existing methods to determine semantic relations between text spans are limited in the field of discourse-level relation recognition. |
| Approach: | They propose to expand the training data set using the corpus of explicitly-related arguments by arbitrarily dropping the overtly presented discourse connectives. |
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Structured Minimally Supervised Learning for Neural Relation Extraction (N19-1)
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| Challenge: | Recent work shows that distant supervision can cause significant label noise when learning from large quantities of unlabeled text. |
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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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Transfer Learning for Entity Recognition of Novel Classes (C18-1)
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| Challenge: | Existing approaches to entity recognition are based on class labels in source and target domains, and many NER corpora only annotate a small number of categories. |
| Approach: | They replicate and extend several past studies on transfer learning for entity recognition. |
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