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Entity-Relation Extraction Framed as Multi-Turn Question Answering

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Entity-Relation Extraction Framed as Multi-Turn Question Answering

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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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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Guoyin Wang, Duo Chai, Mingxin Zhou, Jiwei Li ·

    Entity-Relation Extraction as Multi-Turn Question Answering

    arXiv:1905.05529v5 Announce Type: replace Abstract: In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the task of ident…

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