Entity-Relation Extraction Framed as Multi-Turn Question Answering

Researchers have introduced a novel approach to entity-relation extraction by framing it as a multi-turn question-answering problem. This method leverages machine reading comprehension models and encodes crucial information within question queries. Experiments conducted on the ACE and CoNLL04 datasets demonstrated significant improvements over existing models, achieving state-of-the-art results. The approach was also successfully applied to a new Chinese dataset, RESUME, which requires more complex reasoning for entity dependency extraction. AI

IMPACT This research advances entity-relation extraction techniques, potentially improving information retrieval and knowledge graph construction.

RANK_REASON The cluster contains an academic paper detailing a new methodology for entity-relation extraction. [lever_c_demoted from research: ic=1 ai=1.0]

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• ACE04
• ACE 2005 Multilingual Training Corpus
• Ace Robot
• arXiv
• CONLL04
• Jiwei Li
• /resume

• paper
• other

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