Information Extraction Method Research Articles

A Spectral–Spatial Approach for the Classification of Tree Cover Density in Mediterranean Biomes Using Sentinel-2 Imagery

Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree cover density using Random Forest classifiers. Three spatial information extraction methods are investigated for their capacity to acutely detect canopy cover: two based on Gray-Level Co-Occurrence Matrix (GLCM) features and one based on segment statistics. The research was carried out in three different biomes in Greece, in a total study area of 23,644 km2. Three tree cover classes were considered, namely, non-forest (cover < 15%), open forest (cover = 15%–70%), and closed forest (cover ≥ 70%), based on the requirements set for fuel mapping in Europe. Results indicate that the best approach identified delivers F1-scores ranging 70%–75% for all study areas, significantly improving results over the other alternatives. Overall, the synergistic use of spectral and spatial features derived from Sentinel-2 images highlights a promising approach for the generation of tree cover density information layers in Mediterranean regions, enabling the creation of additional information in support of the detailed mapping of forest fuels.

Vision-guided crack identification and size quantification framework for dam underwater concrete structures

Remotely operated vehicles with cameras provide a non-contact inspection solution for dam underwater structural defect detection. However, manual information extraction methods suffer from problems like labor costs and high misjudgment. This study proposes an integrated dam underwater crack identification and size quantification framework using machine vision and deep learning. First, a real-time automatic detection framework for dam underwater cracks is built via You Only Look Once v5 (YOLOv5), and four different backbone detectors are introduced to balance detection accuracy and speed. Then, the Swin-Transformer module is inserted into the YOLOv5 model to enhance its feature extraction capability and small object detection capability. Next, a method for measuring the true size of cracks was constructed based on deep learning and infrared laser rangefinders. In this study, physical model experiments and actual engineering projects are combined to validate the generalization capability of the proposed crack identification and size quantification method. Experimental results show that the Swin-Transformer-based YOLOv5 model effectively balances detection accuracy and speed with a precision of 0.986, a recall of 0.979, a mean average precision of 0.985, and a frame rate of 68 frames per second in detecting underwater crack images with 768 × 576 pixels. In addition, the proposed method can accurately identify and detect cracks in complex underwater scenes, including obstacle interference, tilt shooting angle, uneven illumination, and turbid water scenarios. Moreover, the method proposed in this paper can quantify the overall size and geometric parameters of underwater cracks with relatively small errors.

Research on Land Use and Land Cover Information Extraction Methods for Remote Sensing Images Based on Improved Convolutional Neural Networks

To address the challenges that convolutional neural networks (CNNs) face in extracting small objects and handling class imbalance in remote sensing imagery, this paper proposes a novel spatial contextual information and multiscale feature fusion encoding–decoding network, SCIMF-Net. Firstly, SCIMF-Net employs an improved ResNeXt-101 deep backbone network, significantly enhancing the extraction capability of small object features. Next, a novel PMFF module is designed to effectively promote the fusion of features at different scales, deepening the model’s understanding of global and local spatial contextual information. Finally, introducing a weighted joint loss function improves the SCIMF-Net model’s performance in extracting LULC information under class imbalance conditions. Experimental results show that compared to other CNNs such as Res-FCN, U-Net, SE-U-Net, and U-Net++, SCIMF-Net improves PA by 0.68%, 0.54%, 1.61%, and 3.39%, respectively; MPA by 2.96%, 4.51%, 2.37%, and 3.45%, respectively; and MIOU by 3.27%, 4.89%, 4.2%, and 5.68%, respectively. Detailed comparisons of locally visualized LULC information extraction results indicate that SCIMF-Net can accurately extract information from imbalanced classes and small objects.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis

Solar cell research aims to improve power conversion efficiency (PCE). This field has an extensive body of literature on the Web of Science. For researchers, it is impossible to understand the development of the entire field comprehensively through traditional reading methods. Knowledge is recorded in the literature by text and numbers. Researchers acquire knowledge through literature surveying, text reading, and thinking. The conversion from text and numbers to knowledge can be automatically completed by machines, which can avoid path‐dependent perspectives. In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. As an example, a knowledge base of organic solar cells (OSCs) is extracted including topic analysis of literature, numerical characteristics of performance, and material information. Seven major research directions of OSCs are identified. The correlations between key performance parameters, including PCE, short‐circuit current density (JSC), open‐circuit voltage (VOC), and fill factor (FF), are revealed from text mining. A donor–acceptor material map of PCE is constructed which provides a road map for OSCs, indicating the bottleneck of this field. Moreover, the method of machine intelligence developed here can be used in any other materials field, aiding a comprehensive understanding of the development quickly.

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

How Does a Generative Large Language Model Perform on Domain-Specific Information Extraction?─A Comparison between GPT-4 and a Rule-Based Method on Band Gap Extraction.

The advent of generative Large Language Models (LLMs) has greatly impacted the field of Natural Language Processing. However, it is inconclusive how generative LLMs perform on domain-specific information extraction tasks. This study compares the performance of GPT-4 and a rule-based information extraction method based on ChemDataExtractor on band gap information extraction, a task that has important implications for the materials science domain. No training data is required for either method, which is desirable because there is a lack of training data in the materials science domain compared with a variety of material information that is of interest. Manual evaluation on 415 randomly selected articles showed that the GPT-4 model achieved a higher level of accuracy in extracting materials' band gap information than the rule-based method (Correctness 87.95% vs 51.08%, Partial correctness 11.33% vs 36.87%, incorrectness 0.72% vs 12.05%). Further analysis of the errors reveals the strengths and weaknesses of the GPT-4 model compared to the rule-based method. The GPT-4 model shows stronger performance in interdependency resolution and complicated material name recognition, while it also has weaknesses in hallucination, identifying band gap values, and identifying band gap types. Revised prompt based on the error analysis leads to improved accuracy for GPT-4. To the best of our knowledge, this study is the first to compare the GPT-4 model and ChemDataExtractor for the band gap extraction task. This study provides evidence to support using generative LLMs for domain-specific information extraction tasks.

Information Extraction of Aviation Accident Causation Knowledge Graph: An LLM-Based Approach

Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. With the continuous development of artificial intelligence technology, leveraging large language models for information extraction and knowledge graph construction has demonstrated significant advantages. This paper proposes an information extraction method for aviation accident causation based on Claude-prompt, which relies on the large-scale pre-trained language model Claude 3.5. Through prompt engineering, combined with a few-shot learning strategy and a self-judgment mechanism, this method achieves automatic extraction of accident-cause entities and their relationships. Experimental results indicate that this approach effectively improves the accuracy of information extraction, overcoming the limitations of traditional methods in terms of accuracy and efficiency in processing complex texts. It provides strong support for subsequently constructing a structured knowledge graph of aviation accident causation and conducting causation analysis of aviation accidents.

Automatic bridge inspection database construction through hybrid information extraction and large language models

Regular bridge inspections generate extensive reports that, while critical for maintenance, often remain underutilized due to their unstructured format. Traditional information extraction methods depend on intricate labeling systems that commonly require time-consuming and labor-intensive labeling. This paper presents a novel bridge inspection database construction method leveraging LLM-assisted information extraction. First, we introduce the pseudo-labelling method using a closed-source LLM to generate high-quality data. Then we propose the hybrid extraction pipeline to extract relevant information segments and process them by a generation-based IE model, fine-tuned on pseudo-labeled data. Finally, the extracted data is used to construct the bridge inspection database. The proposed method, validated with real-world data, not only demonstrates higher extraction precision than the closed-source LLM used for pseudo-labeling but also outperforms traditional methods in both data preparation time and extraction accuracy. This approach provides a scalable solution for more proactive and data-driven bridge maintenance strategies.

Novel deep‐learning model for chemical process fault detection based on DCW transformer

AbstractIn this study, a double‐channel convolutional neural network and weighted (DCW) transformer model is proposed to address the problem of insufficient extraction of local information and no attention to channel‐step information in the traditional transformer model. First, a double‐channel information extraction method is proposed, so both the channel‐step and time‐step information achieve attention; second, the local information in the time and channel dimension of the data is extracted from deep and multiple scales, improving the feature extraction capability for local information; third, the long distance dependency relationship of the data is preserved by the attention mechanism, hence, the global correlation of the data is extracted effectively; finally, using the Gumbel‐SoftMax function, the weights of the time‐step and channel‐step feature information are assigned, so the extracted feature information has been optimized. The proposed method was applied for the penicillin fermentation process to verify its efficacy. Experimental results show that the proposed method achieved a better fault detection accuracy, outperforming the existing models. Further ablation experiments were conducted to demonstrate the effectiveness of each component of the proposed model.

Multimodal Deep Learning Network for Differentiating Between Benign and Malignant Pulmonary Ground Glass Nodules.

This study aimed to establish a multimodal deep-learning network model to enhance the diagnosis of benign and malignant pulmonary ground glass nodules [GGNs]. Retrospective data on pulmonary GGNs were collected from multiple centers across China, including North, Northeast, Northwest, South, and Southwest China. The data were divided into a training set and a validation set in an 8:2 ratio. In addition, a GGN dataset was also obtained from our hospital database and used as the test set. All patients underwent chest computed tomography [CT], and the final diagnosis of the nodules was based on postoperative pathological reports. The Residual Network [ResNet] was used to extract imaging data, the Word2Vec method for semantic information extraction, and the Self Attention method for combining imaging features and patient data to construct a multimodal classification model. Then, the diagnostic efficiency of the proposed multimodal model was compared with that of existing ResNet and VGG models and radiologists. The multicenter dataset comprised 1020 GGNs, including 265 benign and 755 malignant nodules, and the test dataset comprised 204 GGNs, with 67 benign and 137 malignant nodules. In the validation set, the proposed multimodal model achieved an accuracy of 90.2%, a sensitivity of 96.6%, and a specificity of 75.0%, which surpassed that of the VGG [73.1%, 76.7%, and 66.5%] and ResNet [78.0%, 83.3%, and 65.8%] models in diagnosing benign and malignant nodules. In the test set, the multimodal model accurately diagnosed 125 [91.18%] malignant nodules, outperforming radiologists [80.37% accuracy]. Moreover, the multimodal model correctly identified 54 [accuracy, 80.70%] benign nodules, compared to radiologists' accuracy of 85.47%. The consistency test comparing radiologists' diagnostic results with the multimodal model's results in relation to postoperative pathology showed strong agreement, with the multimodal model demonstrating closer alignment with gold standard pathological findings [Kappa=0.720, P<0.01]. The multimodal deep learning network model exhibited promising diagnostic effectiveness in distinguishing benign and malignant GGNs and, therefore, holds potential as a reference tool to assist radiologists in improving the diagnostic accuracy of GGNs, potentially enhancing their work efficiency in clinical settings.

Extracting lung cancer staging descriptors from pathology reports: A generative language model approach

BackgroundIn oncology, electronic health records contain textual key information for the diagnosis, staging, and treatment planning of patients with cancer. However, text data processing requires a lot of time and effort, which limits the utilization of these data. Recent advances in natural language processing (NLP) technology, including large language models, can be applied to cancer research. Particularly, extracting the information required for the pathological stage from surgical pathology reports can be utilized to update cancer staging according to the latest cancer staging guidelines. ObjectivesThis study has two main objectives. The first objective is to evaluate the performance of extracting information from text-based surgical pathology reports and determining pathological stages based on the extracted information using fine-tuned generative language models (GLMs) for patients with lung cancer. The second objective is to determine the feasibility of utilizing relatively small GLMs for information extraction in a resource-constrained computing environment. MethodsLung cancer surgical pathology reports were collected from the Common Data Model database of Seoul National University Bundang Hospital (SNUBH), a tertiary hospital in Korea. We selected 42 descriptors necessary for tumor-node (TN) classification based on these reports and created a gold standard with validation by two clinical experts. The pathology reports and gold standard were used to generate prompt-response pairs for training and evaluating GLMs which then were used to extract information required for staging from pathology reports. ResultsWe evaluated the information extraction performance of six trained models as well as their performance in TN classification using the extracted information. The Deductive Mistral-7B model, which was pre-trained with the deductive dataset, showed the best performance overall, with an exact match ratio of 92.24% in the information extraction problem and an accuracy of 0.9876 (predicting T and N classification concurrently) in classification. ConclusionThis study demonstrated that training GLMs with deductive datasets can improve information extraction performance, and GLMs with a relatively small number of parameters at approximately seven billion can achieve high performance in this problem. The proposed GLM-based information extraction method is expected to be useful in clinical decision-making support, lung cancer staging and research.

Development of an Internet-based Product-related Child Injury Textual Data Platform (IPCITDP) in China.

Internet-based media stories provide valuable information for emerging risks of product-related child injury prevention and control, but critical methodological challenges and high costs of data acquisition and processing restrict practical use by stakeholders. We constructed a data platform through literature reviews and multi-round research group discussions. Developed components included standard search strategies, filtering criteria, textual document classification, information extraction standards and a keyword dictionary. We used ten thousand manually labelled media stories to validate the textual document classification model, which was established using the Bidirectional Encoder Representation from Transformers (BERT). Multiple information extraction methods based on natural language processing algorithms were adopted to extract data for 29 structured variables from media stories. They were evaluated through manual validation of 1000 media stories about product-related child injury. We mapped the geographic distribution of media sources and media-reported product-related child injury events. We developed an internet-based product-related child injury textual data platform, IPCITDP, consisting of four layers - automatic data search and acquisition, data processing, data storage, and data application - concerning product-related child injury online media stories in China. Each layer occurred daily. External validation demonstrated high performance for the BERT classification model we established (accuracy = 0.9703) and the combined information extraction strategies (accuracy >0.70 for 25 variables). As of 31 December 2023, IPCITDP collected 35 275 eligible product-related child injury reports from 13 261 news media websites or social media platform accounts which were geographically located across all 31 mainland Chinese provinces and covered over 97% of prefecture-level cities. The injury cases in IPCITDP were typically reported several months or years earlier than official announcements about the product-related child injury risks. Our data platform added data concerning 15 supplementary variables that the national product-related injury surveillance system lacks. Two examples demonstrate the value of IPCITDP in supplementing existing data and providing early epidemiological detection of emerging signals concerning product-related child injury: magnetic beads and electric self-balancing scooters. Our data platform provides injury data that can support early detection of new product-related child injury characteristics and supplement existing data sources to reduce the burden of product-related injury among Chinese children.

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China.

Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations. This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health. This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed. The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions. Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Analysis of Fault Events in Rail Transit Vehicle Traction Systems Based on Knowledge Graph Reasoning

Fault text records provide detailed information on faults and handling steps, which are valuable for fault analysis. However, the different individuals’ recording styles can lead to ambiguities, and it is challenging to uncover potential fault associations in complex systems. To address these issues, this paper proposes a novel method for fault information extraction and analysis. Firstly, to tackle the problem of ambiguous boundaries between entities in fault texts, an integration algorithm is employed to accurately recognize fault entities considering contextual semantic features to establish fault knowledge graph (FKG). Then, a Relational Graph Convolutional Networks (RGCN) is improved with Graph Attention Networks (GAT) for sparse nodes caused by specific types of faults, to dynamically adjust the weight distribution of node learning, inferring potential links within the graph. The proposed method was validated using actual fault records from the traction system of rail transit vehicles, and contributes a reference for the mining and analysis of fault records in complex systems.

COfEE: A comprehensive ontology for event extraction from text

Large volumes of data are constantly being published on the web; however, the majority of this data is often unstructured, making it difficult to comprehend and interpret. To extract meaningful and structured information from such data, researchers and practitioners have turned to Information Extraction (IE) methods. One of the most challenging IE tasks is Event Extraction (EE), which involves extracting information related to specific incidents and their associated actors from text. EE has broad applications, including building a knowledge base, information retrieval, summarization, and online monitoring systems. Over the past few decades, various event ontologies, such as ACE, CAMEO, and ICEWS, have been developed to define event forms, actors, and dimensions of events observed in text. However, these ontologies have some limitations, such as covering only a few topics like political events, having inflexible structures in defining argument roles, lacking analytical dimensions, and insufficient gold-standard data. To address these concerns, we propose a new event ontology, COfEE, which integrates expert domain knowledge, previous ontologies, and a data-driven approach for identifying events from text. COfEE comprises two hierarchy levels (event types and event sub-types) that include new categories related to environmental issues, cyberspace, criminal activity, and natural disasters that require real-time monitoring. In addition, dynamic roles are defined for each event sub-type to capture various dimensions of events. The proposed ontology is evaluated on Wikipedia events, and it is shown to be comprehensive and general. Furthermore, to facilitate the preparation of gold-standard data for event extraction, we present a language-independent online tool based on COfEE. A gold-standard dataset annotated by ten human experts consisting of 24,000 news articles in Persian according to the COfEE ontology is also prepared. To diversify the data, news articles from the Wikipedia event portal and the 100 most popular Persian news agencies between 2008 and 2021 were collected. Finally, we introduce a supervised method based on deep learning techniques to automatically extract relevant events and their corresponding actors.

Leveraging Medical Knowledge Graphs and Large Language Models for Enhanced Mental Disorder Information Extraction

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management.

Research on privacy leakage of celebrity's ID card number based on real‐name authentication

AbstractThe Internet real‐name system is widely implemented among Chinese Internet users, and many commonly used apps in China exist the functions of real‐name authentication. However, our study found that many apps do not have effective restrictions on user's operations of real‐name authentication, resulting in users being able to frequently perform unsuccessful real‐name authentication attempts. This vulnerability can help an attacker crack celebrity's ID card number by enumeration attacks, and a feasible cracking method was proposed in this paper. First, the information of birth date, birth place, and life experiences of a celebrity is collected from the platforms that display celebrities' personal information (e.g., Wikipedia, Baidu Baike, etc.). In this process, an information extraction method is used to infer permanent residences from life experiences. Then, the possible ID card numbers of a celebrity can be constructed by using the information of birth date, birth place, and permanent residences. Finally, these possible ID card numbers will be verified by sending requests to platforms that have vulnerabilities in the function of user real‐name authentication, until the real ID card number of a celebrity being cracked. This paper conducted cracking experiments on two groups of celebrities. The first group of celebrities is collected from the news events of privacy leakage that were publicly available online, and the second group of celebrities is randomly selected from two encyclopedia platforms. The experimental results showed that the success rate of cracking the ID card numbers of celebrities is 53.9%, which verified the effectiveness of the proposed cracking method. Besides, this paper proposed some security precaution suggestions to solve this security problem, and the implementation, feasibility, potential impact, expected effectiveness of these measures were also analyzed. To our knowledge, our paper is the first to point out the issue of privacy leakage of celebrity's ID card number caused by apps' real‐name authentication functions in China. We believe that our research will attract widespread attention from society regarding celebrity's privacy information protection.

A few-shot word-structure embedded model for bridge inspection reports learning

Intelligent bridge maintenance requires the comprehensive utilization of inspection records, as they contain valuable insights into structures’ long-term service conditions. To efficiently focus and utilize this data from extensive reports, reliable extraction methods are highly sought after. However, the heavy reliance on large amounts of manually annotated data limits the applicability and practicability of existing information extraction methods from bridge inspection reports, especially given the non-uniformity in report formats and expressions. To address this issue, this study proposed a few-shot information extraction model for bridge inspection reports understanding, comprising Word and Structure Integration Embeddings, Bi-directional Long Short-Term Memory (BiLSTM), and Condition Random Field (CRF). The model’s strength lies in its easy-to-implement word-structure embedding approach, which combines domain-specific word representations and sentence structure information. Specifically, the bridge inspection-domain pre-trained language model was further pre-trained and fine-tuned to obtain word embeddings, containing prior knowledge of the domain and tasks. Moreover, a novel encoding method was designed to generate sentence structure embeddings from dependency syntactic analysis results, providing textual representation information. Finally, the integrated word-structure embeddings, created by aligning dimensions for concatenation, were fed into the BiLSTM-CRF architecture to capture contextual dependencies and constrain extraction results. Empirical evaluations conducted on four few-shot datasets with 10, 30, 50, and 100 samples demonstrate that the proposed model achieved high accuracy and F1 score, outperforming prior methods, general domain models, and large language models. Specifically, in a dataset containing 50 sentences, our model achieved an accuracy of up to 0.9357 and an F1 score of 0.8683, representing an average increase of 38.4% higher than these methods. Ablation experiments revealed the contributions of each model component. These results suggest that the proposed model can accurately extract key information from bridge inspection reports even with limited training data scenarios, thereby facilitating applications such as structural condition evaluation and maintenance decision-making.

Multi-attention semantic segmentation method for forest information extraction in hilly and mountainous areas

Multi-attention semantic segmentation method for forest information extraction in hilly and mountainous areas