Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach [PDF]
JMIR Formative ResearchBackgroundExtractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets.
Duy-Anh Nguyen +5 more
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ISPRS International Journal of Geo-InformationGeographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding.
Yuting Zhang +5 more
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Improving deep learning method for biomedical named entity recognition by using entity definition information [PDF]
BMC Bioinformatics, 2021 Background Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type.
Ying Xiong +6 more
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Answer Extraction Method for Reading Comprehension Based on Frame Semantics and GraphStructure [PDF]
Jisuanji kexue, 2023 Machine reading comprehension is one of the most challenging tasks in the field of natural language processing.With the continuous development of deep learning technology and the release of large-scale MRC datasets,the performance of MRC models keep ...
YANG Zhizhuo, XU Lingling, Zhang Hu, LI Ru
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Learn a prior question-aware feature for machine reading comprehension
Frontiers in Physics, 2022 Machine reading comprehension aims to train machines to comprehend a given context and then answer a series of questions according to their understanding of the context. It is the cornerstone of conversational reading comprehension and question answering
Yu Zhang, Bo Shen, Xing Cao
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IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable questions.
Rifki Afina Putri, Alice Oh
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Exploring Machine Reading Comprehension for Continuous Questions via Subsequent Question Completion
IEEE Access, 2021 In recent years, the Sq-MRC (machine reading comprehension for separate questions) task, where the questioner poses a separate question each time, has experienced rapid development.
Kaijing Yang, Xin Zhang, Dongmei Chen
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Review of Conversational Machine Reading Comprehension
Jisuanji kexue yu tansuo, 2021 Machine reading comprehension (MRC) is a research field driven by datasets. The task of MRC is to make the machine correctly answer relevant questions on the basis of understanding the natural language text.
LI Kun, LI Yanling, LIN Min
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Multi-Document Neural Reading Comprehension Based on Bi-Directional Attention Mechanism [PDF]
Jisuanji gongcheng, 2020 Machine Reading Comprehension(MRC) is a question and answer task that automatically generates or extracts corresponding answers for a given text and specific questions.This task is of great significance to evaluating the understanding of computer systems
TANG Hongxuan, WU Kaili, ZHU Mengmeng, HONG Yu
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NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension
CoRR, 2023 Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpora and incorporating search engines.
Yuxiang Zhang +4 more
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