A New Entity Extraction Method Based on Machine Reading Comprehension

• URL: http://arxiv.org/abs/2108.06444v1
• Date: Sat, 14 Aug 2021 02:11:41 GMT
• Title: A New Entity Extraction Method Based on Machine Reading Comprehension
• Authors: Xiaobo Jiang, Kun He, Jiajun He and Guangyu Yan
• Abstract summary: This paper presents an effective MRC-based entity extraction model-MRC-I2DP. It uses the proposed gated attention-attracting mechanism to adjust the restoration of each part of the text pair. It also uses the proposed 2D probability coding module, TALU function and mask mechanism to strengthen the detection of all possible targets of the target.
• Score: 4.92025078254413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Entity extraction is a key technology for obtaining information from massive texts in natural language processing. The further interaction between them does not meet the standards of human reading comprehension, thus limiting the understanding of the model, and also the omission or misjudgment of the answer (ie the target entity) due to the reasoning question. An effective MRC-based entity extraction model-MRC-I2DP, which uses the proposed gated attention-attracting mechanism to adjust the restoration of each part of the text pair, creating problems and thinking for multi-level interactive attention calculations to increase the target entity It also uses the proposed 2D probability coding module, TALU function and mask mechanism to strengthen the detection of all possible targets of the target, thereby improving the probability and accuracy of prediction. Experiments have proved that MRC-I2DP represents an overall state-of-the-art model in 7 from the scientific and public domains, achieving a performance improvement of 2.1% ~ 10.4% compared to the model model in F1.

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