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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Electronic Medical Record Entity Recognition via Machine Reading Comprehension and Biaffine
Discrete Dynamics in Nature and Society, 2021 The entity recognition of Chinese electronic medical record is of great significance to medical decision-making. The main process of entity recognition is sequence tagging, which has problems such as nested entity and boundary prediction.
Jun Cao +6 more
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FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
Applied Sciences, 2022 Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering ...
Addi Ait-Mlouk +3 more
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Machine Reading Comprehension Based On Multi-headed attention Model
MATEC Web of Conferences, 2018 Machine Reading Comprehension (MRC) refers to the task that aims to read the context through the machine and answer the question about the original text, which needs to be modeled in the interaction between the context and the question.
Xu Hui, Zhang Shichang, Jiang Jie
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Application of machine reading comprehension techniques for named entity recognition in materials science [PDF]
Journal of CheminformaticsMaterials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these
Zihui Huang +8 more
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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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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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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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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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Machine Reading Comprehension Framework Based on Self-Training for Domain Adaptation
IEEE Access, 2021 Machine reading comprehension (MRC) is a type of question answering mechanism in which a computer reads documents and answers related questions. The accuracies of recent MRC systems surpass those of humans.
Hyeon-Gu Lee, Youngjin Jang, Harksoo Kim
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