Unsupervised technical phrase extraction by incorporating structure and position information

Zhou, Peng; Jiang, Xin; Zhao, Shu

doi:10.1016/j.eswa.2024.123140

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…In addition, the experiment provides the sentences of some patents with labels of technical field, technical issues, technical solutions, and technical effects. In addition, the proposed PKECC also adopts zero-one loss function to accelerate convergence, which is listed in Equation (11).…”

Section: Parameter Settingsmentioning

confidence: 99%

“…In addition, various researches have introduced additional information to update weights of graph models, such as point mutual information [7], word meaning information [8], location information, and topic information [9], in order to effectively identify keywords. However, existing unsupervised keyword extraction methods not only ignore low-frequency words and highly related words [10], but also have difficulty extracting main technical words [11].…”

Section: Introductionmentioning

confidence: 99%

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A Patent Keyword Extraction Method Based on Corpus Classification

Sun,

Chen,

Zhang

et al. 2024

Mathematics

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The keyword extraction of patents is crucial for technicians to master the trends of technology. Traditional keyword extraction approaches only handle short text like title or claims, but ignore the comprehensive meaning of the description. This paper proposes a novel patent keyword extraction method based on corpus classification (PKECC), which simulates the patent understanding methods of human patent examiners. First of all, a corpus classification model based on multi-level attention mechanism adopts the Bert model and hierarchical attention mechanism to classify the sentences of patent description into four parts including technical field, technical problem, technical solution, and technical effect. Then, the proposed keyword extraction method based on the fusion of BiLSTM and CRF is incorporated to extract keywords from the four parts. The proposed PKECC simulates understanding style of patent examiner by extracting keywords from the description. Meanwhile, PKECC may reduce the complexity of extracting keywords from a long text and improve the accuracy of keyword extraction. The proposed PKECC is compared with 5 traditional or state-of-the-art models and achieves better accuracy, F1 score and recall rate; its recall rate is above 62%, its accuracy reaches over 84%, and the F1 score arrives at 69%. In addition, the experimental results shows the proposed PKECC has a better universality in keyword extraction.

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Section: Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Patent Keyword Extraction Method Based on Corpus Classification

Sun,

Chen,

Zhang

et al. 2024

Mathematics

View full text Add to dashboard Cite

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A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system

Bai,

Kang,

Wang

et al. 2024

DIGITAL HEALTH

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Objective This study aims to address the limitations of current clinical methods in predicting delivery mode by constructing a multimodal neural network-based model. The model utilizes data from a digital twin-empowered labor monitoring system, including computerized cardiotocography (cCTG), ultrasound (US) examination data, and electronic health records (EHRs) of pregnant women. Methods The model integrates three modalities of data from 105 pregnant women (76 vaginal deliveries and 29 cesarean deliveries) at the Department of Obstetrics and Gynecology of The First Affiliated Hospital of Jinan University, Guangzhou, China. It employs a hybrid architecture of a convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) to compress the data into a single feature vector for each patient. Results The designed model achieves a cross-validation accuracy of 93.33%, an F1-score of 86.26%, an area under the receiver operating characteristic curve of 97.10%, and a Brier Score of 6.67%. Importantly, while cCTG and EHRs are crucial for labor management, the integration of US imaging data significantly enhances prediction accuracy. Conclusion The findings of this study suggest that the developed multimodal model is a promising tool for predicting delivery mode and provides a comprehensive approach to intrapartum maternal and fetal health monitoring. The integration of multi-source data, including real-time information, holds potential for further improving the algorithm's predictive accuracy as the volume of analyzed data increases. This could be highly beneficial for dynamically fusing data from different sources throughout the maternal and fetal health lifecycle, from pregnancy to delivery.

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Unsupervised technical phrase extraction by incorporating structure and position information

Cited by 2 publications

References 21 publications

A Patent Keyword Extraction Method Based on Corpus Classification

A Patent Keyword Extraction Method Based on Corpus Classification

A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system

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