Multi-label biomedical question classification for lexical answer type prediction

Wasim, Muhammad; Asim, Muhammad Nabeel; Khan, Muhammad Usman Ghani; Mahmood, Waqar

doi:10.1016/j.jbi.2019.103143

Cited by 24 publications

(12 citation statements)

References 22 publications

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“…Multimodal QASs use multiple input modalities from users to answer questions, such as text and images [8]. The recent developments in the fields of biomedicine, electronic publishing, and computing technology have contributed to the rapid growth in the biomedical literature available online to medical practitioners [9,10]. The medical practitioners can assess biomedical literature using the PUBMED database, which consists of more than 32 million citations from life science journals, online books, etc.…”

Section: Introductionmentioning

confidence: 99%

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

Mutabazi

Tang

et al. 2021

Applied Sciences

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The advent of Question Answering Systems (QASs) has been envisaged as a promising solution and an efficient approach for retrieving significant information over the Internet. A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources. However, the medical domain receives less attention due to the shortage of medical datasets. Although Electronic Health Records (EHRs) are empowering the field of Medical Question-Answering (MQA) by providing medical information to answer user questions, the gap is still large in the medical domain, especially for textual-based sources. Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored. Furthermore, an in-depth analysis of deep learning approaches used in different MQA system tasks was provided. Finally, the different critical challenges posed by MQA systems were highlighted, and recommendations to effectively address them in forthcoming MQA systems were given out.

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Section: Introductionmentioning

confidence: 99%

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

Mutabazi

Tang

et al. 2021

Applied Sciences

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“…This algorithm ranks labels via pairwise comparison. The label power-set (LP) algorithm [19] treats each element in the power-set of a label set as one class and therefore transforms a multi-label classification problem into a multi-class classification problem. The random k-labelsets (RAkEL) algorithm establishes a set of LP classifiers, trains each LP classifier on various random subsets of the label set and obtains a new instance via LP classifier voting [20].…”

Section: Introductionmentioning

confidence: 99%

A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network

et al. 2020

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Digital signage is an important outdoor advertising medium in cities. However, advertising on digital signage often lacks pertinence. Thus, it is important to introduce an accurate digital signage audience classification method to facilitate targeted advertising. In this study, a multi-label classification model based on a backpropagation (BP) neural network and the Huff model, referred to as the Huff-BP model, is proposed to investigate digital signage audience classification. A case study is performed on outdoor digital signage within the 6th Ring Road in Beijing, China, and economic census, population census, average housing price, social media check-in and the centrality of traffic networks as research data. The data are divided into 100 × 100-1,000 × 1,000 m normal grids. Multi-label classification modelling factors for various grid scales are constructed. The BP neural network classification algorithm is improved to solve the multilabel classification problem. In addition, an improved Huff model is used to calculate the digital signage influence values between each grid cell and integrated into the improved BP neural network to classify modelling factors at various scales. Finally, four metrics are used to examine the effectiveness of the proposed model. The results show that the Huff-BP-based multi-label classification model achieves relatively good classification results, and the digital signage audiences are mainly concentrated within the 4th Ring Road and near the 5th Ring Road.

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“…Figure 1 shows two examples of questions in the dataset, which attribution includes question explanation, keywords and test information, etc. Figure 1: Examples from DT-QDC dataset Some other datasets exist such as: a genuine grade-school level, multiple-choice science questions dataset is contributed by (Clark et al, 2018), Wasim et al (Wasim et al, 2019) submitted a Multi-label biomedical question dataset, and Li et al (Li and Roth, 2002) proposed a free-form questions dataset, yet these datasets only have question text and label, and their volume is relatively lacking. In comparison, our dataset is larger and richer in attributes, which is very valuable for future research communities to design, evaluate, and understand questions.…”

Section: Introductionmentioning

confidence: 99%

DT-QDC: A Dataset for Question Comprehension in Online Test

Wu¹,

Yang²,

Yung³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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As education switches from the traditional classroom environment to online education and assessment, teachers could not follow students' learning behavior closely which leads to inaccurate assessment of students' knowledge. A well-defined method to precisely measure difficulty of questions is critical as it can guide teachers in assessing students' performance and help provide customized attention to students. In this paper, we explore the concept of question difficulty and share our new Chinese Driving Test Question Difficulty Comprehension (DT-QDC) dataset. Different from the existing question datasets, we mainly design for question evaluation and comprehension in online testing, so each question has enriched attributes and difficulty label. Additional attributes such as keywords, chapter, and question type would allow models to understand questions more precisely. We proposed the Ordinal Regression Multi-Source BERT (ORMS-BERT) model to assess the difficulty of the question, which outperforms different baselines by 6.77% on F1-score and 15.92% on MAE, 28.26% on MSE on the DT-QDC dataset, laying the foundation for the question difficulty comprehension task.

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Multi-label biomedical question classification for lexical answer type prediction

Cited by 24 publications

References 22 publications

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network

DT-QDC: A Dataset for Question Comprehension in Online Test

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