CSR-SVM: Compositional semantic representation for intelligent identification of engineering change documents based on SVM

Wu, Yi; Liu, Fagui; Zheng, Lailei; Wu, Xinjie; Lai, Chengqi

doi:10.1016/j.aei.2023.102050

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…How to extract massive multi-source and heterogeneous metadata information according to the metadata standard framework is also a key problem. With the rapid development of machine learning and deep learning, support vector machines (SVM) [17], decision tree, random forest [18], and other machine learning algorithms are used to extract structured information from text. Deep learning techniques such as recurrent neural networks (RNNs) [19], long short-term memory networks (LSTMs) [20], transformer, etc., have also made significant progress in metadata extraction tasks [21].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

Wang,

Shi,

Yang

et al. 2024

IJGI

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The development of information technology has led to massive, multidimensional, and heterogeneously sourced disaster data. However, there’s currently no universal metadata standard for managing natural disasters. Common pre-training models for information extraction requiring extensive training data show somewhat limited effectiveness, with limited annotated resources. This study establishes a unified natural disaster metadata standard, utilizes self-trained universal information extraction (UIE) models and Python libraries to extract metadata stored in both structured and unstructured forms, and analyzes the results using the Word2vec-Kmeans cluster algorithm. The results show that (1) the self-trained UIE model, with a learning rate of 3 × 10−4 and a batch_size of 32, significantly improves extraction results for various natural disasters by over 50%. Our optimized UIE model outperforms many other extraction methods in terms of precision, recall, and F1 scores. (2) The quality assessments of consistency, completeness, and accuracy for ten tables all exceed 0.80, with variances between the three dimensions being 0.04, 0.03, and 0.05. The overall evaluation of data items of tables also exceeds 0.80, consistent with the results at the table level. The metadata model framework constructed in this study demonstrates high-quality stability. (3) Taking the flood dataset as an example, clustering reveals five main themes with high similarity within clusters, and the differences between clusters are deemed significant relative to the differences within clusters at a significance level of 0.01. Overall, this experiment supports effective sharing of disaster data resources and enhances natural disaster emergency response efficiency.

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