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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 up to compared to the model model in F1.
The development of natural language processing (NLP) in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension t
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence s
Span-extraction reading comprehension models have made tremendous advances enabled by the availability of large-scale, high-quality training datasets. Despite such rapid progress and widespread application, extractive reading comprehension datasets i
Span extraction is an essential problem in machine reading comprehension. Most of the existing algorithms predict the start and end positions of an answer span in the given corresponding context by generating two probability vectors. In this paper, w
Recent studies on machine reading comprehension have focused on text-level understanding but have not yet reached the level of human understanding of the visual layout and content of real-world documents. In this study, we introduce a new visual mach