Intelligent question answering method for construction safety hazard knowledge based on deep semantic mining

Tian, Dan; Li, Mingchao; Ren, Qingying; Zhang, Xiaojian; Han, Shuai; Shen, Yang

doi:10.1016/j.autcon.2022.104670

Cited by 29 publications

(5 citation statements)

References 40 publications

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“…Sánchez and Hartlieb (2020) and Rylnikova et al (2017) similarly emphasized the efficiency and scalability of these frameworks in managing real-time data streams. The ability of AI to provide timely and accurate insights is crucial for sectors like finance, where real-time sentiment analysis of market news can inform investment decisions (Tian et al, 2023). Enhancing user interaction through voice-based queries and interactive visual elements is another significant finding.…”

Section: Discussionmentioning

confidence: 99%

A Review of Utilizing Natural Language Processing and Ai for Advanced Data Visualization in Real-Time Analytics

Uddin

2024

GMJ

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This review explores the integration of Natural Language Processing (NLP) and Artificial Intelligence (AI) in enhancing data visualization for real-time analytics. In an era characterized by the exponential growth of data, traditional static visualizations are increasingly inadequate for meeting the demands of real-time decision-making. NLP and AI offer sophisticated tools to dynamically interpret and visualize data, turning vast amounts of raw information into actionable insights across various domains. This paper synthesizes current research, methodologies, and applications of NLP and AI in data visualization, highlighting key advancements such as enhanced data interpretability, real-time data processing capabilities, and improved user interaction through natural language queries and interactive elements. It also addresses the challenges and limitations associated with implementing these technologies, including computational complexity, data quality issues, and ethical considerations. The review identifies significant trends and future directions, such as the integration of augmented and virtual reality (AR/VR) and the use of generative AI models, which promise to further advance the field. By providing a comprehensive overview of the current state of NLP and AI in data visualization, this paper aims to inform and guide future research and development efforts in leveraging these technologies for more effective and efficient data-driven decision-making.

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Section: Discussionmentioning

confidence: 99%

A Review of Utilizing Natural Language Processing and Ai for Advanced Data Visualization in Real-Time Analytics

Uddin

2024

GMJ

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“…The proposed method enhanced the contextual representation through the combination of BERT and static domain dictionaries and resolved the problem of semantic matching by implementing the hierarchical cross-attention network. Moreover, Tian et al [16] introduced an intelligent question-answering system for safety hazard knowledge based on deep semantic mining. In this study, BERT, Bidirectional Gated Recurrent Unit (BiGRU), and self-attention mechanisms have been integrated for effective feature extraction, and a Siamese neural network is implemented for answer selection.…”

Section: -Literature Reviewmentioning

confidence: 99%

Multilingual Question Answering for Malaysia History with Transformer-based Language Model

Lim,

Lee,

Lim

et al. 2024

Emerg Sci J

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In natural language processing (NLP), a Question Answering System (QAS) refers to a system or model that is designed to understand and respond to user queries in natural language. As we navigate through the recent advancements in QAS, it can be observed that there is a paradigm shift of the methods used from traditional machine learning and deep learning approaches towards transformer-based language models. While significant progress has been made, the utilization of these models for historical QAS and the development of QAS for Malay language remain largely unexplored. This research aims to bridge the gaps, focusing on developing a Multilingual QAS for history of Malaysia by utilizing a transformer-based language model. The system development process encompasses various stages, including data collection, knowledge representation, data loading and pre-processing, document indexing and storing, and the establishment of a querying pipeline with the retriever and reader. A dataset with a collection of 100 articles, including web blogs related to the history of Malaysia, has been constructed, serving as the knowledge base for the proposed QAS. A significant aspect of this research is the use of the translated dataset in English instead of the raw dataset in Malay. This decision was made to leverage the effectiveness of well-established retriever and reader models that were trained on English data. Moreover, an evaluation dataset comprising 100 question-answer pairs has been created to evaluate the performance of the models. A comparative analysis of six different transformer-based language models, namely DeBERTaV3, BERT, ALBERT, ELECTRA, MiniLM, and RoBERTa, has been conducted, where the effectiveness of the models was examined through a series of experiments to determine the best reader model for the proposed QAS. The experimental results reveal that the proposed QAS achieved the best performance when employing RoBERTa as the reader model. Finally, the proposed QAS was deployed on Discord and equipped with multilingual support through the incorporation of language detection and translation modules, enabling it to handle queries in both Malay and English. Doi: 10.28991/ESJ-2024-08-02-019 Full Text: PDF

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“…By analyzing online comments, understanding the opinions of netizens and discovering the real demands of the public, we can find the essential reasons behind the phenomenon and then propose powerful improvement strategies. Deep mining and analysis techniques based on online reviews have been widely used in various research fields [47][48][49][50][51]; some of the literature is organized as follows.…”

Section: Civil Aviation Safety Level Perception Researchmentioning

confidence: 99%

Safety Perception Evaluation of Civil Aviation Based on Weibo Posts in China: An Enhanced Large-Scale Group Decision-Making Framework

Feng

Xin

Xiong

et al. 2023

Int. J. Fuzzy Syst.

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The massive spread of COVID-19 and the crash of China Eastern Airlines MU5735 have negatively impacted the public’s perception of civil aviation safety, which further affects the progress of the civil aviation industry and economic growth. The aim of research is to investigate the public’s perception of China’s civil aviation safety and give the authorities corresponding suggestions. First, we use online comment collection and sentiment analysis techniques to construct a novel evaluation index system reflecting the public’s greatest concern for civil aviation safety. Then, we propose two novel large-scale group decision-making (LSGDM) models for aggregating evaluation: (1) K -means clustering with a novel distance measure for evaluators combined with unsupervised K -means clustering in two-stage, (2) unsupervised K-means clustering for evaluators combined with unsupervised K -means clustering for processing evaluation in two-stage. Finally, we compare the characteristics of different models and use the average of the two models as the final evaluation results.

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Intelligent question answering method for construction safety hazard knowledge based on deep semantic mining

Cited by 29 publications

References 40 publications

A Review of Utilizing Natural Language Processing and Ai for Advanced Data Visualization in Real-Time Analytics

A Review of Utilizing Natural Language Processing and Ai for Advanced Data Visualization in Real-Time Analytics

Multilingual Question Answering for Malaysia History with Transformer-based Language Model

Safety Perception Evaluation of Civil Aviation Based on Weibo Posts in China: An Enhanced Large-Scale Group Decision-Making Framework

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