Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
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| Challenge: | Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. |
| Approach: | They propose a framework that unifies learning of RE and KBE models . the framework is based on a relation extraction task that uses a KB relation to a phrase . |
| Outcome: | The proposed framework unifies learning of RE and KBE models, leading to significant improvements over the state-of-the-art RE framework. |
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
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| Challenge: | Existing approaches to extract relationships between entities are based on sentence-level tasks, but they do not consider domain knowledge, which are assumed to be known to the reader when documents are authored. |
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Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping (D19-1)
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| Challenge: | Relation extraction (RE) is an important information extraction task that seeks to detect and classify semantic relationships between entities. |
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| Challenge: | Existing models that perform explicit on-task training of graph embeddings are inadequate. |
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Global-to-Local Neural Networks for Document-Level Relation Extraction (2020.emnlp-main)
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| Challenge: | Relation extraction (RE) aims to identify the semantic relations between named entities in text. |
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