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Purpose To assess the performance of a local open-source large language model (LLM) in various information extraction tasks from real-life emergency brain MRI reports. Materials and Methods All consecutive emergency brain MRI reports written in 2022 from a French quaternary center were retrospectively reviewed. Two radiologists identified MRI scans that were performed in the emergency department for headaches. Four radiologists scored the reports conclusions as either normal or abnormal. Abnormalities were labeled as either headache-causing or incidental. Vicuna (LMSYS Org), an open-source LLM, performed the same tasks. Vicunas performance metrics were evaluated using the radiologists consensus as the reference standard. Results Among the 2398 reports during the study period, radiologists identified 595 that included headaches in the indication (median age of patients, 35 years [IQR, 26-51 years]; 68% [403 of 595] women). A positive finding was reported in 227 of 595 (38%) cases, 136 of which could explain the headache. The LLM had a sensitivity of 98.0% (95% CI: 96.5, 99.0) and specificity of 99.3% (95% CI: 98.8, 99.7) for detecting the presence of headache in the clinical context, a sensitivity of 99.4% (95% CI: 98.3, 99.9) and specificity of 98.6% (95% CI: 92.2, 100.0) for the use of contrast medium injection, a sensitivity of 96.0% (95% CI: 92.5, 98.2) and specificity of 98.9% (95% CI: 97.2, 99.7) for study categorization as either normal or abnormal, and a sensitivity of 88.2% (95% CI: 81.6, 93.1) and specificity of 73% (95% CI: 62, 81) for causal inference between MRI findings and headache. Conclusion An open-source LLM was able to extract information from free-text radiology reports with excellent accuracy without requiring further training. Keywords: Large Language Model (LLM), Generative Pretrained Transformers (GPT), Open Source, Information Extraction, Report, Brain, MRI Supplemental material is available for this article. Published under a CC BY 4.0 license. See also the commentary by Akinci DAntonoli and Bluethgen in this issue.

Objective Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associated symptom burden, we extract structured, semantic representations of medical problem and drug information from the clinical narratives of oncology notes. Materials and Methods We present Clinical concept Annotations for Cancer Events and Relations (CACER), a novel corpus with fine-grained annotations for over 48 000 medical problems and drug events and 10 000 drug-problem and problem-problem relations. Leveraging CACER, we develop and evaluate transformer-based information extraction models such as Bidirectional Encoder Representations from Transformers (BERT), Fine-tuned Language Net Text-To-Text Transfer Transformer (Flan-T5), Large Language Model Meta AI (Llama3), and Generative Pre-trained Transformers-4 (GPT-4) using fine-tuning and in-context learning (ICL). Results In event extraction, the fine-tuned BERT and Llama3 models achieved the highest performance at 88.2-88.0 F1, which is comparable to the inter-annotator agreement (IAA) of 88.4 F1. In relation extraction, the fine-tuned BERT, Flan-T5, and Llama3 achieved the highest performance at 61.8-65.3 F1. GPT-4 with ICL achieved the worst performance across both tasks. Discussion The fine-tuned models significantly outperformed GPT-4 in ICL, highlighting the importance of annotated training data and model optimization. Furthermore, the BERT models performed similarly to Llama3. For our task, large language models offer no performance advantage over the smaller BERT models. Conclusions We introduce CACER, a novel corpus with fine-grained annotations for medical problems, drugs, and their relationships in clinical narratives of oncology notes. State-of-the-art transformer models achieved performance comparable to IAA for several extraction tasks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of "machine recommendation-human review–machine incremental learning" was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods. [ABSTRACT FROM AUTHOR]

This research evaluates a number of hybrid recurrent neural network (RNN) architectures for classifying sequential sentences in biomedical abstracts. The architectures include long short-term memory (LSTM), bidirectional LSTM (BI-LSTM), gated recurrent unit (GRU), and bidirectional GRU (BIGRU) models, all of which are combined with the universal sentence encoder (USE). The investigation assesses their efficacy in categorizing sentences into predefined classes: background, objective, method, result, and conclusion. Each RNN variant is used with the pre-trained USE as word embeddings to find complex sequential relationships in biomedical text. Results demonstrate the adaptability and effectiveness of these hybrid architectures in discerning diverse sentence functions. This research addresses the need for improved literature comprehension in biomedicine by employing automated sentence classification techniques, highlighting the significance of advanced hybrid algorithms in enhancing text classification methodologies within biomedical research. [ABSTRACT FROM AUTHOR]

As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT's applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its comprehensive analysis of the existing literature, addressing a critical gap in understanding ChatGPT's adaptability, limitations, and optimal application. In this paper, we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to direct our search process and seek relevant studies. Our review reveals ChatGPT's significant potential in enhancing various NLP tasks. Its adaptability in information extraction tasks, sentiment analysis, and text classification showcases its ability to comprehend diverse contexts and extract meaningful details. Additionally, ChatGPT's flexibility in annotation tasks reduces manual efforts and accelerates the annotation process, making it a valuable asset in NLP development and research. Furthermore, GPT-4 and prompt engineering emerge as a complementary mechanism, empowering users to guide the model and enhance overall accuracy. Despite its promising potential, challenges persist. The performance of ChatGPT needs to be tested using more extensive datasets and diverse data structures. Subsequently, its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues. [ABSTRACT FROM AUTHOR]

Objectives Alzheimer's disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. We aim to automate the extraction of specific sleep-related patterns, such as snoring, napping, poor sleep quality, daytime sleepiness, night wakings, other sleep problems, and sleep duration, from clinical notes of AD patients. These sleep patterns are hypothesized to play a role in the incidence of AD, providing insight into the relationship between sleep and AD onset and progression. Materials and Methods A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192 000 de-identified clinical notes of 7266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based natural language processing (NLP) algorithm, machine learning models, and large language model (LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Results The annotated dataset of 482 patients comprised a predominantly White (89.2%), older adult population with an average age of 84.7 years, where females represented 64.1%, and a vast majority were non-Hispanic or Latino (94.6%). Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of positive predictive value (PPV), the rule-based NLP algorithm achieved the highest PPV scores for daytime sleepiness (1.00) and sleep duration (1.00), while the machine learning models had the highest PPV for napping (0.95) and bad sleep quality (0.86), and LLAMA2 with finetuning had the highest PPV for night wakings (0.93) and sleep problem (0.89). Discussion Although sleep information is infrequently documented in the clinical notes, the proposed rule-based NLP algorithm and LLM-based NLP algorithms still achieved promising results. In comparison, the machine learning-based approaches did not achieve good results, which is due to the small size of sleep information in the training data. Conclusion The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD but could be extended to general sleep information extraction for other diseases. [ABSTRACT FROM AUTHOR]

Background: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results: We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions: Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive. [ABSTRACT FROM AUTHOR]

Document-level Event Argument Extraction (DEAE) aims to extract structural event knowledge composed of arguments and roles beyond the sentence level. Existing methods mainly focus on designing prompts and using Abstract Meaning Representation (AMR) graph structure as additional features to enrich event argument representation. However, two challenges still remain: (1) the long-range dependency between event trigger and event arguments and (2) the distracting context in the document towards an event that can mislead the argument classification. To address these issues, we propose a novel document-level event argument extraction model named AMR Parser and Sparse Representation (APSR). Specifically, APSR sets inter- and intra-sentential encoders to capture the contextual information in different scopes. Especially, in the intra-sentential encoder, APSR designs three types of sparse event argument attention mechanisms to extract the long-range dependency. Then, APSR constructs AMR semantic graphs, which capture the interactions among concepts well. Finally, APSR fuses the inter- and intra-sentential representations and predicts what role a candidate span plays. Experimental results on the RAMS and WikiEvents datasets demonstrate that APSR achieves a superior performance compared with competitive baselines in terms of F1 by 1.27 % and 3.12 % , respectively. [ABSTRACT FROM AUTHOR]

Service discovery and composition are crucial tasks in the development of Web services, it is designed to select the appropriate services for each task and ensure that services are called in the correct order. Unlike traditional methods, we aim to solve the service composition problem from a new perspective by developing a novel service composition using semantics based natural language descriptions. The proposed algorithm employs a combination of natural language processing and semantic-based techniques to extract the functional semantics of service datasets and understand the user context. By parsing the user's request, the method extracts possible sub-queries contained within the request and identifies the appropriate combination of sub-queries. Leveraging the BM25 algorithm and the ESIM semantic matching model, the method identifies atomic services with high correlation with sub-queries and determines the composable atomic services set according to the defined composition constraints. Experimental results validate the efficacy of our method. [ABSTRACT FROM AUTHOR]

This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs’ performance in the domain of construction and inference. Empirically, our findings suggest that LLMs, represented by GPT-4, are more suited as inference assistants rather than few-shot information extractors. Specifically, while GPT-4 exhibits good performance in tasks related to KG construction, it excels further in reasoning tasks, surpassing fine-tuned models in certain cases. Moreover, our investigation extends to the potential generalization ability of LLMs for information extraction, leading to the proposition of a Virtual Knowledge Extraction task and the development of the corresponding VINE dataset. Based on these empirical findings, we further propose AutoKG, a multi-agent-based approach employing LLMs and external sources for KG construction and reasoning. We anticipate that this research can provide invaluable insights for future undertakings in the field of knowledge graphs. [ABSTRACT FROM AUTHOR]

The article focuses on improving Named Entity Recognition (NER) within Information Extraction (IE), crucial for enhancing precision in Information Retrieval (IR) systems. It introduces Single-Stream Reasoner (SSR), a type-agnostic solution for NER tasks, addressing challenges such as unconventional predictions and inefficient processing, achieving state-of-the-art results across multiple benchmarks and demonstrating efficiency in convergence and low-resource scenarios.

The article focuses on enhancing Cross-domain Named Entity Recognition (NER) by recognizing the discrepant transferability between entity detection and type prediction tasks. It introduces a novel paradigm called divide-and-transfer, employing separate functional modules for each task to improve domain transfer effectiveness.

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Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present TREK (Toward Document-Level Relation Extraction in Korean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of TREK. Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language's properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis. [ABSTRACT FROM AUTHOR]

Objectives To enhance the performance of large language models (LLMs) in biomedical natural language processing (BioNLP) by introducing a domain-specific instruction dataset and examining its impact when combined with multi-task learning principles. Materials and Methods We created the BioInstruct , comprising 25 005 instructions to instruction-tune LLMs (LLaMA 1 and 2, 7B and 13B version). The instructions were created by prompting the GPT-4 language model with 3-seed samples randomly drawn from an 80 human curated instructions. We employed Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. We then evaluated these instruction-tuned LLMs on several BioNLP tasks, which can be grouped into 3 major categories: question answering (QA), information extraction (IE), and text generation (GEN). We also examined whether categories (eg, QA, IE, and generation) of instructions impact model performance. Results and Discussion Comparing with LLMs without instruction-tuned, our instruction-tuned LLMs demonstrated marked performance gains: 17.3% in QA on average accuracy metric, 5.7% in IE on average F1 metric, and 96% in Generation tasks on average GPT-4 score metric. Our 7B-parameter instruction-tuned LLaMA 1 model was competitive or even surpassed other LLMs in the biomedical domain that were also fine-tuned from LLaMA 1 with vast domain-specific data or a variety of tasks. Our results also show that the performance gain is significantly higher when instruction fine-tuning is conducted with closely related tasks. Our findings align with the observations of multi-task learning, suggesting the synergies between 2 tasks. Conclusion The BioInstruct dataset serves as a valuable resource and instruction tuned LLMs lead to the best performing BioNLP applications. [ABSTRACT FROM AUTHOR]

With the proliferation of digital platforms, the dissemination of literary works has encountered unprecedented challenges, particularly concerning copyright infringement and unauthorized use. This study introduces a comprehensive framework for copyright protection and risk assessment, specifically tailored to online literary works. The framework employs advanced CNN based information extraction (IE) techniques coupled with machine learning (ML) algorithms to identify, classify, and protect literary content against copyright violations. Firstly, we delineate a novel CNN-Decision tree-based IE methodology that systematically harvests metadata and textual content from various online repositories. This process is designed to detect and index online literary works, extracting pertinent features such as authorship, publication date, and textual patterns. Following the extraction, the study utilizes natural language processing (NLP) to analyze and compare content, pinpointing potential instances of copyright infringement by identifying significant overlaps and stylistic similarities with registered works. Subsequently, we introduce a risk assessment model developed through supervised machine learning. This model is trained on a labelled dataset comprising instances of both copyrighted and non-copyrighted works, along with known cases of copyright infringement. By analyzing the extracted features, the model assesses the probability of infringement, categorizing risks into high, medium, and low categories. This stratification allows stakeholders to prioritize enforcement actions and resources efficiently. The study further explores the implementation of various ML algorithms, including decision trees, support vector machines, and neural networks, to determine the most effective approach for copyright protection in the literary domain. We evaluate the models based on accuracy, precision, recall, and F1-score metrics, emphasizing their capacity to generalize and operate in dynamic, real-world environments. [ABSTRACT FROM AUTHOR]

With the rapid advancement of Internet technology and the increasing volume of police reports, relying solely on extensive human labor and traditional natural language processing methods for key element extraction has become impractical. Applying advanced technologies such as large language models to improve the effectiveness of police report extraction has become an inevitable trend in the field of police data analysis. This study addresses the characteristics of Chinese police reports and the need to extract key elements by employing large language models specific to the public security domain for entity extraction. Several lightweight (6/7b) open-source large language models were tested as base models. To enhance model performance, LoRA fine-tuning was employed, combined with data engineering approaches. A zero-shot data augmentation method based on ChatGPT and prompt engineering techniques tailored for police reports were proposed to further improve model performance. The key police report data from a certain city in 2019 were used as a sample for testing. Compared to the base models, prompt engineering improved the F1 score by approximately 3%, while fine-tuning led to an increase of 10–50% in the F1 score. After fine-tuning and comparing different base models, the Baichuan model demonstrated the best overall performance in extracting key elements from police reports. Using the data augmentation method to double the data size resulted in an additional 4% increase in the F1 score, achieving optimal model performance. Compared to the fine-tuned universal information extraction (UIE) large language model, the police report entity extraction model constructed in this study improved the F1 score for each element by approximately 5%, with a 42% improvement in the F1 score for the "organization" element. Finally, ChatGPT was employed to align the extracted entities, resulting in a high-quality entity extraction outcome. [ABSTRACT FROM AUTHOR]

Clinical named entity extraction (NER) based on deep learning gained much attention among researchers and data analysts. This paper proposes a NER approach to extract valuable Parkinson’s disease-related information. To develop an effective NER method and to handle problems in disease data analytics, a unique NER technique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NER approach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NER with effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NER method. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1- measure of 91.6% [ABSTRACT FROM AUTHOR]

Information extraction is the foundation of knowledge graph construction, and relation extraction, as a key process and core step of information extraction, aims to locate entities from text data and recognize semantic links between entities. Therefore, improving the efficiency of relation extraction can effectively improve the quality of information extraction, which affects the construction of knowledge graph and subsequent downstream tasks. Relation extraction can be categorized into sentence-level relation extraction and document-level relation extraction according to the length of the extracted text. The two levels of extraction methods have their own advantages and disadvantages in different application scenarios: sentence-level relation extraction is suitable for application scenarios with smaller datasets, while document-level relation extraction is suitable for scenarios such as news event analysis, long reports or articles with relational mining. Unlike the existing relation extraction, this paper first introduces the basic concept of relation extraction and the development history of the field in recent years, lists the datasets used in the two levels of relation extraction, and gives an overview of the characteristics of the datasets. Then, this paper elaborates on the sentence-level relation extraction and the document-level relation extraction respectively, summarizes the advantages and disadvantages of different levels of relation extraction, and analyses the performance and limitations of the representative models in each method. Finally, this paper summarizes the problems in the current research field and looks forward to future development of relation extraction.

Abstract Recent advances in deep learning and natural language processing (NLP) have broadened opportunities for automatic text processing in the medical field. However, the development of models for low-resource languages like French is challenged by limited datasets, often due to legal restrictions. Large-scale training of medical imaging models often requires extracting labels from radiology text reports. Current methods for report labeling primarily rely on sophisticated feature engineering based on medical domain knowledge or manual annotations by radiologists. These methods can be labor-intensive. In this work, we introduce a BERT-based approach for the efficient labeling of French mammogram image reports. Our method leverages both the expansive scale of existing rule-based systems and the precision of radiologist annotations. Our experimental results showcase the superiority of the proposed approach. It was initially fine-tuned on a limited dataset of radiologist annotations. Then, it underwent training on annotations generated by a rule-based labeler. Our findings reveal that our final model, MammoBERT, significantly outperforms the rule-based labeler while simultaneously reducing the necessity for radiologist annotations during training. This research not only advances the state of the art in medical image report labeling but also offers an efficient and effective solution for large-scale medical imaging model development.

Abstract 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.

Abstract Background Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to the task of clinical information extraction from cancer pathology reports, extracting the following six properties: main site, subsite, laterality, histology, behavior, and grade. Results We evaluated the clinical task performance of the FastMPN models in terms of micro- and macro-averaged F1 scores. A comparison was performed with the multi-task convolutional neural network (MT-CNN) model. Results show that the FastMPN model is equivalent to or better than the MT-CNN. Conclusions Our implementation revealed that our FastMPN model, which is based on the PyTorch platform, can train a large corpus (667,290 training samples) with 202,373 unique words in less than 3 minutes per epoch using one NVIDIA V100 hardware accelerator. Our experiments demonstrated that using this implementation, the clinical task performance scores of information extraction related to tumors from cancer pathology reports were highly competitive.

In the biomedical domain, the surge in article volume means valuable insights on diseases and symptoms are often hidden in academic literature. Leveraging natural language processing and text mining to sift through biomedical texts is vital for advancing early diagnosis, enhancing clinical decision support systems, and refining ontologies. Particularly for respiratory diseases, which share symptoms like fever, cough, and breathlessness, differentiating between diseases based on symptoms is crucial for early and accurate diagnosis. This study introduces a method for extracting disease-symptom relationships, aiming to identify rare symptoms not mentioned in health resources but potentially related to diseases, and to ascertain the association strength between diseases and symptoms. Initially, a hybrid entity recognition approach was proposed for identifying diseases and symptoms in medical texts. Then, the diseases and symptoms were normalized, and their associations ranked by semantic similarity scores. Evaluated on a dataset of respiratory diseases, including academic article abstracts on asthma, bronchitis, pulmonary embolism, and COVID-19, the study uncovered rare symptoms in addition to characteristic ones. The dot product similarity method proved more effective, achieving an average similarity score of 0.66, in establishing the associations between diseases and symptoms, revealing the significance of literature validation in identifying rare symptom-disease relations. [ABSTRACT FROM AUTHOR]

Magnesium (Mg) based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio. However, to fully harness the potential of Mg alloys, structured analytics are essential to gain valuable insights from centuries of accumulated knowledge. Efficient information extraction from the vast corpus of scientific literature is crucial for this purpose. In this work, we introduce MagBERT, a BERT-based language model specifically trained for Mg-based materials. Utilizing a dataset of approximately 370,000 abstracts focused on Mg and its alloys, MagBERT is designed to understand the intricate details and specialized terminology of this domain. Through rigorous evaluation, we demonstrate the effectiveness of MagBERT for information extraction using a fine-tuned named entity recognition (NER) model, named MagNER. This NER model can extract mechanical, microstructural, and processing properties related to Mg alloys. For instance, we have created an Mg alloy dataset that includes properties such as ductility, yield strength, and ultimate tensile strength (UTS), along with standard alloy names. The introduction of MagBERT is a novel advancement in the development of Mg-specific language models, marking a significant milestone in the discovery of Mg alloys and textual information extraction. By making the pre-trained weights of MagBERT publicly accessible, we aim to accelerate research and innovation in the field of Mg-based materials through efficient information extraction and knowledge discovery.

Abstract Background Stigma surrounding substance use can result in severe consequences for physical and mental health. Identifying situations in which stigma occurs and characterizing its impact could be a critical step toward improving outcomes for individuals experiencing stigma. As part of a larger research project with the goal of informing the development of interventions for substance use disorder, this study leverages natural language processing methods and a theory-informed approach to identify and characterize manifestations of substance use stigma in social media data. Methods We harvested social media data, creating an annotated corpus of 2,214 Reddit posts from subreddits relating to substance use. We trained a set of binary classifiers; each classifier detected one of three stigma types: Internalized Stigma, Anticipated Stigma, and Enacted Stigma, from the Stigma Framework. We evaluated hybrid models that combine contextual embeddings with features derived from extant lexicons and handcrafted lexicons based on stigma theory, and assessed the performance of these models. Then, using the trained and evaluated classifiers, we performed a mixed-methods analysis to quantify the presence and type of stigma in a corpus of 161,448 unprocessed posts derived from subreddits relating to substance use. Results For all stigma types, we identified hybrid models (RoBERTa combined with handcrafted stigma features) that significantly outperformed RoBERTa-only baselines. In the model’s predictions on our unseen data, we observed that Internalized Stigma was the most prevalent stigma type for alcohol and cannabis, but in the case of opioids, Anticipated Stigma was the most frequent. Feature analysis indicated that language conveying Internalized Stigma was predominantly characterized by emotional content, with a focus on shame, self-blame, and despair. In contrast, Enacted Stigma and Anticipated involved a complex interplay of emotional, social, and behavioral features. Conclusion Our main contributions are demonstrating a theory-based approach to extracting and comparing different types of stigma in a social media dataset, and employing patterns in word usage to explore and characterize its manifestations. The insights from this study highlight the need to consider the impacts of stigma differently by mechanism (internalized, anticipated, and enacted), and enhance our current understandings of how each stigma mechanism manifests within language in particular cognitive, emotional, social, and behavioral aspects.

Satellite remote sensing, characterized by extensive coverage, frequent revisits, and continuous monitoring, provides essential data support for addressing global challenges. Over the past six decades, thousands of Earth observation satellites and sensors have been deployed worldwide. These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies. This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential. In this paper, we describe the development of GEOSatDB, the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model. A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information. The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors. The global Earth observation capabilities of 195 countries worldwide have been analyzed in detail, and a concrete use case along with an associated query demonstration is presented. This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observation resources.

With the latest advances in natural language processing technologies, multi-participant conversation summarization features are now embedded in the most widely used collaboration platforms offered by industry leaders such as Microsoft, Google, and Zoom. This allows employees to streamline their work and increase efficiency by summarizing long chat threads. In this study, an attempt has been made to perceive summarized chat conversations as a tool for knowledge discovery and reusable information extraction within an organization in general or during projects. To this end, recent scientific articles have been reviewed to identify the most effective techniques and approaches for summarizing chat threads and conversations alongside the challenges and peculiarities of collaborative text-based communication. In addition, significant attention has been paid to the further utilization of the extracted information to represent the knowledge for further reuse.

Images of building facades, rich in design information, are important for architectural studies. In recent years, numerous studies have explored the automatic recognition and annotation of basic elements (e.g. windows) in these images. However, most application of the recognized elements remains at the pixel level, such as 3D reconstruction, and the design information hidden behind the elements cannot be processed using common data analysis methods because the annotations are still unstructured data. This prevents us from further discovering the composition pattern of building facades from the images. To enable the recognized elements to be analyzed, a framework for facade design information extraction (FDIE) from annotated building facade images based on graph theory is proposed in this paper. The design information is divided into three categories, i.e. graphic, relationship, and sum information, and extracted as structured data. An image restoration test demonstrates that more than 95% of the design information can be extracted using the FDIE. These structured data provide an important basis for intelligent analysis of composition pattern. Several examples and cases partly illustrate the application prospects of the extracted information. Other graph-based analysis methods are also expected to be applied to the extracted design information.

Abstract Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.

Abstract In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs financial losses amounting to millions annually. If harnessed effectively, this data has the potential to substantially boost operational efficiency. Traditional methods for extracting information have their limitations; however, solutions powered by artificial intelligence (AI) could provide a more fitting alternative. There is an evident gap in scholarly research concerning a comprehensive evaluation of AI-driven techniques for the extraction of information from unstructured content. This systematic literature review aims to identify, assess, and deliberate on prospective research directions within the field of unstructured document information extraction. It has been observed that prevailing extraction methods primarily depend on static patterns or rules, often proving inadequate when faced with complex document structures typically encountered in real-world scenarios, such as medical records. Datasets currently available to the public suffer from low quality and are tailored for specific tasks only. This underscores an urgent need for developing new datasets that accurately reflect complex issues encountered in practical settings. The review reveals that AI-based techniques show promise in autonomously extracting information from diverse unstructured documents, encompassing both printed and handwritten text. Challenges arise, however, when dealing with varied document layouts. Proposing a framework through hybrid AI-based approaches, this review envisions processing a high-quality dataset for automatic information extraction from unstructured documents. Additionally, it emphasizes the importance of collaborative efforts between organizations and researchers to address the diverse challenges associated with unstructured data analysis.

Standardization is one of the key steps to analyze locomotive overhaul data with a focus on reliability-centered maintenance (RCM). However, traditional manual methods encounter challenges such as small sample sizes, non-standardized data formats, analytical complexities, and high labour costs, hindering the achievement of data standardization. Large language models (LLM), featuring powerful performance in natural language processing comprehension and handling complex tasks, have made great academic and industrial progress in recent years. This study initially investigated the application performance of LLMs in information extraction from locomotive overhaul data, with the following three reveals, as the universal information extraction (UIE) LLM is suitable for information extraction in the field of locomotive overhaul; expanding the size of locomotive data helps improve the UIE performance in information extraction from locomotive overhaul data; balancing the types of fault labels does not notably help improve this performance. Subsequent explorations concentrated on difficulties in data annotation. The script writing method was utilized for automated annotation of data, and ChatGLM was leveraged to standardize locomotive overhaul data, yielding Bleu-4, Rouge-1, Rouge-2, and Rouge-L metrics of 86.87%, 89.60%, 87.54%, and 94.26%, respectively, in alignment with the requirements of engineering applications. Further developments introduced an auxiliary data standardization pre-processing tool to streamline the standardization process by encapsulating the LLM.

This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize the extracted information into required entities, adjusting parameters if entities were not correctly extracted based on manual annotations. Despite the diverse formats of medical report images in the dataset, all in Russian, this serves as a conceptual example of information extraction (IE) that can be easily extended to other languages.

Covert channel is a method to hide information in public communication channels for covert transmission, aiming at ensuring data transmission security and making third parties unaware of the covert communication process. However, with the continuous progress of traffic analysis technology, traditional network covert channels can no longer guarantee their security. Blockchain, as an emerging technology that combines cryptography, consensus algorithms, and P2P networks, has characteristics such as decentralization, traceability, anonymity, immutability, and security, making it an ideal platform for constructing covert channels. This paper first introduces blockchain technology, elaborates on the development process of covert channels, researches and summarizes the existing classification methods of covert channels. Then it summarizes the general model of blockchain covert channel on the basis of traditional network covert channel and divides the process of constructing covert channel into two main stages: information transmission and information extraction. Next, starting from the six-layer architecture of blockchain, the blockchain covert channels are analyzed in depth and the blockchain covert channels are divided into six categories according to different levels and subdivided based on the structure and mechanism of each layer, which can help better understand the construction process of blockchain covert channels and explore potential covert channels. Finally, this paper discusses the limitations of blockchain technology for covert channels and proposes future research directions.

Background: Stigma surrounding substance use can result in severe consequences for physical and mental health. Identifying situations in which stigma occurs and characterizing its impact could be a critical step toward improving outcomes for individuals experiencing stigma. As part of a larger research project with the goal of informing the development of interventions for substance use disorder, this study leverages natural language processing methods and a theory-informed approach to identify and characterize manifestations of substance use stigma in social media data. Methods: We harvested social media data, creating an annotated corpus of 2,214 Reddit posts from subreddits relating to substance use. We trained a set of binary classifiers; each classifier detected one of three stigma types: Internalized Stigma, Anticipated Stigma, and Enacted Stigma, from the Stigma Framework. We evaluated hybrid models that combine contextual embeddings with features derived from extant lexicons and handcrafted lexicons based on stigma theory, and assessed the performance of these models. Then, using the trained and evaluated classifiers, we performed a mixed-methods analysis to quantify the presence and type of stigma in a corpus of 161,448 unprocessed posts derived from subreddits relating to substance use. Results: For all stigma types, we identified hybrid models (RoBERTa combined with handcrafted stigma features) that significantly outperformed RoBERTa-only baselines. In the model's predictions on our unseen data, we observed that Internalized Stigma was the most prevalent stigma type for alcohol and cannabis, but in the case of opioids, Anticipated Stigma was the most frequent. Feature analysis indicated that language conveying Internalized Stigma was predominantly characterized by emotional content, with a focus on shame, self-blame, and despair. In contrast, Enacted Stigma and Anticipated involved a complex interplay of emotional, social, and behavioral features. Conclusion: Our main contributions are demonstrating a theory-based approach to extracting and comparing different types of stigma in a social media dataset, and employing patterns in word usage to explore and characterize its manifestations. The insights from this study highlight the need to consider the impacts of stigma differently by mechanism (internalized, anticipated, and enacted), and enhance our current understandings of how each stigma mechanism manifests within language in particular cognitive, emotional, social, and behavioral aspects. [ABSTRACT FROM AUTHOR]

With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed energy resources, fine-grained perception of intelligent sensing devices, and the inherent stochasticity of source-load dynamics, a massive amount of raw data is being recorded and accumulated in the data center. Valuable information is often dispersed across different paragraphs of the raw data, making it challenging to extract effectively. Distribution substation inspection plays a crucial role in ensuring the safe operation of the power system. Traditional methods for inspection report text classification typically rely on manual judgment and accumulated experience, resulting in low efficiency and a significant misjudgment rate. Therefore, this paper proposes a text classification method for inspection reports based on the pre-trained BERT-TextRCNN model. By utilizing the dense connection between the BERT embedding layer and the neural network, the proposed method improves the accuracy of matching long texts. This article collected 2,831 maintenance data for the first quarter of 2023 from the distribution room, including approximately 58 environmental testing data, 738 environmental box testing data, approximately 672 distribution room testing data, and approximately 1,363 box type substation testing data. A text corpus was constructed for experiments. Experimental results demonstrate that the proposed model automatically classifies a large volume of manually recorded inspection report data based on time, location, and faults, achieving a classification accuracy of 94.7%, precision of 92%, recall of 92%, and F1 score of 90.3%. [ABSTRACT FROM AUTHOR]

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset's value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence. [ABSTRACT FROM AUTHOR]

Organisations heavily dependent on paper documents still spend a significant amount of time managing a large volume of documents. An intelligent document management system (DMS) is presented to automate the processing of tax and administrative documents. The proposed system fills a gap in the landscape of practical tools in the field of DMS and advances the state of the art. This system represents a complex process of integrated AI-powered technologies that creates an ontology, extracts information from documents, defines profiles, maps the extracted data in RDF format, and applies inference through a reasoning engine. The DMS was designed to help all those companies that manage their clients' tax and administrative documents daily. Automation speeds up the management process so that companies can focus more on value-added services. The system was tested in a case study that focused on the preparation of tax returns. The results demonstrated the efficacy of the system in providing document management service. [ABSTRACT FROM AUTHOR]

• Trained a language model (MagBERT) for magnesium data extraction. • Trained on ∼370K abstracts focused on Mg and its alloys. • Ability to extract mechanical, microstructure, and processing properties. • Publicly available model to boost magnesium materials research. Magnesium (Mg) based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio. However, to fully harness the potential of Mg alloys, structured analytics are essential to gain valuable insights from centuries of accumulated knowledge. Efficient information extraction from the vast corpus of scientific literature is crucial for this purpose. In this work, we introduce MagBERT, a BERT-based language model specifically trained for Mg-based materials. Utilizing a dataset of approximately 370,000 abstracts focused on Mg and its alloys, MagBERT is designed to understand the intricate details and specialized terminology of this domain. Through rigorous evaluation, we demonstrate the effectiveness of MagBERT for information extraction using a fine-tuned named entity recognition (NER) model, named MagNER. This NER model can extract mechanical, microstructural, and processing properties related to Mg alloys. For instance, we have created an Mg alloy dataset that includes properties such as ductility, yield strength, and ultimate tensile strength (UTS), along with standard alloy names. The introduction of MagBERT is a novel advancement in the development of Mg-specific language models, marking a significant milestone in the discovery of Mg alloys and textual information extraction. By making the pre-trained weights of MagBERT publicly accessible, we aim to accelerate research and innovation in the field of Mg-based materials through efficient information extraction and knowledge discovery. [Display omitted] [ABSTRACT FROM AUTHOR]

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Objectives The integration of these preventive guidelines with Electronic Health Records (EHRs) systems, coupled with the generation of personalized preventive care recommendations, holds significant potential for improving healthcare outcomes. Our study investigates the feasibility of using Large Language Models (LLMs) to automate the assessment criteria and risk factors from the guidelines for future analysis against medical records in EHR. Materials and Methods We annotated the criteria, risk factors, and preventive medical services described in the adult guidelines published by United States Preventive Services Taskforce and evaluated 3 state-of-the-art LLMs on extracting information in these categories from the guidelines automatically. Results We included 24 guidelines in this study. The LLMs can automate the extraction of all criteria, risk factors, and medical services from 9 guidelines. All 3 LLMs perform well on extracting information regarding the demographic criteria or risk factors. Some LLMs perform better on extracting the social determinants of health, family history, and preventive counseling services than the others. Discussion While LLMs demonstrate the capability to handle lengthy preventive care guidelines, several challenges persist, including constraints related to the maximum length of input tokens and the tendency to generate content rather than adhering strictly to the original input. Moreover, the utilization of LLMs in real-world clinical settings necessitates careful ethical consideration. It is imperative that healthcare professionals meticulously validate the extracted information to mitigate biases, ensure completeness, and maintain accuracy. Conclusion We developed a data structure to store the annotated preventive guidelines and make it publicly available. Employing state-of-the-art LLMs to extract preventive care criteria, risk factors, and preventive care services paves the way for the future integration of these guidelines into the EHR. [ABSTRACT FROM AUTHOR]

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution. [ABSTRACT FROM AUTHOR]

Event extraction aims to extract human-interest information from massive amounts of unstructured text. Currently, most existing event extraction methods are based on general corpora and rarely consider domain- specific prior knowledge. Moreover, most methods cannot handle well the case where multiple events exist in the same document, and they perform poorly when faced with a large number of negative examples. To address these issues, this paper proposes a model called Fin-PTPCG based on Fin-BERT (financial bidirectional encoder representation from Transformers) and PTPCG (pseudo-trigger-aware pruned complete graph). This method fully utilizes the expression ability of the Fin-BERT pre- training model and incorporates domain- specific prior knowledge during the encoding stage. In the event detection module, multiple binary classifiers are stacked to ensure that the model can effectively identify the situation of multiple events in a document and screen out negative examples. Combined with the decoding module of the PTPCG model, entities are extracted and connected into a complete graph and pruned by calculating a similarity matrix. The problem of unlabeled triggers is solved by selecting pseudo-triggers. Finally, the event extraction is achieved by the event classifier. This method achieves a 0.7 and 3.7 percentage points improvement in F1 score compared to the baselines on the ChFinAnn and Duee-fin datasets for the event extraction task. [ABSTRACT FROM AUTHOR]

In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs financial losses amounting to millions annually. If harnessed effectively, this data has the potential to substantially boost operational efficiency. Traditional methods for extracting information have their limitations; however, solutions powered by artificial intelligence (AI) could provide a more fitting alternative. There is an evident gap in scholarly research concerning a comprehensive evaluation of AI-driven techniques for the extraction of information from unstructured content. This systematic literature review aims to identify, assess, and deliberate on prospective research directions within the field of unstructured document information extraction. It has been observed that prevailing extraction methods primarily depend on static patterns or rules, often proving inadequate when faced with complex document structures typically encountered in real-world scenarios, such as medical records. Datasets currently available to the public suffer from low quality and are tailored for specific tasks only. This underscores an urgent need for developing new datasets that accurately reflect complex issues encountered in practical settings. The review reveals that AI-based techniques show promise in autonomously extracting information from diverse unstructured documents, encompassing both printed and handwritten text. Challenges arise, however, when dealing with varied document layouts. Proposing a framework through hybrid AI-based approaches, this review envisions processing a high-quality dataset for automatic information extraction from unstructured documents. Additionally, it emphasizes the importance of collaborative efforts between organizations and researchers to address the diverse challenges associated with unstructured data analysis. [ABSTRACT FROM AUTHOR]

Structured meshes are becoming increasingly popular as a discretization technique in industries where solution accuracy is of paramount importance. However, generating structured meshes directly on the model surface presents a challenge. Consequently, many researchers are now exploring mapping methods for structured mesh generation. In this context, parameterization algorithms are vital as they map the model surface from 3D space to a 2D parametric domain. Existing parameterization algorithms, despite their advancements, still face limitations in preserving symmetry information from the input model. To address this issue, we propose a symmetry preserving parameterization algorithm based on geometric constraint. This algorithm utilizes plane reflection symmetry transformation to identify the main symmetry plane of the model and calculate the symmetry factor. Based on the fixed points and the symmetry factor, a conformal parametric symmetry plane is obtained. Furthermore, we develop a surface structured mesh generation system to provide engineers with a tool that can rapidly generate meshes. Experimental results demonstrate that our proposed algorithm achieves higher quality results compared to other parameterization algorithms. [ABSTRACT FROM AUTHOR]