A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning
Applied Sciences, 2021 To address the problem of poor semantic reasoning of models in multiple-choice Chinese machine reading comprehension (MRC), this paper proposes an MRC model incorporating multi-granularity semantic reasoning.
Yu Dai, Yufan Fu, Lei Yang
doaj +2 more sources
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
doaj +2 more sources
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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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
doaj +3 more sources
Visualizing attention zones in machine reading comprehension models. [PDF]
STAR Protoc, 2022 The attention mechanism plays an important role in the machine reading comprehension (MRC) model. Here, we describe a pipeline for building an MRC model with a pretrained language model and visualizing the effect of each attention zone in different ...
Cui Y, Zhang WN, Liu T.
europepmc +2 more sources
BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task. [PDF]
Bioinformatics, 2022 ABSTRACTMotivationBiomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets.
Mahbub M +3 more
europepmc +6 more sources
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
doaj +1 more source
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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A Framework for Evaluation of Machine Reading Comprehension Gold Standards [PDF]
, 2020 Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text. While neural MRC systems gain popularity and achieve noticeable performance, issues are being raised with the methodology used to establish their ...
Schlegel, Viktor; id_orcid +4 more
core +6 more sources