Semi-structured Text Research Articles

An Intelligent Framework of Equipment Fault Diagnosis Based on Knowledge Graph

Equipment fault information typically exhibits the characteristics of fragmentation and diversified structure. The existing fault diagnosis methods are incapable of fully exploiting the prior knowledge and expert knowledge within the field, and the diagnosis results are overly one-sided. Given that it is challenging to obtain effective fault diagnostic knowledge for complex equipment and complex faults, this paper proposes the application of the domain knowledge graph (KG) for fault diagnosis. Different from the existing fault diagnosis methods involving the KG, our framework consists of two major parts. The first part is to construct an equipment fault KG from semi-structured and unstructured text. We further enrich the graph knowledge through knowledge completion, which can furnish high-quality knowledge sources for downstream applications. The second part is to employ the built fault KG either online or offline for fault diagnosis. We offer two approaches: the deep learning plus KG approach; the question-answering approach. The former not only guarantees the diagnostic accuracy but also provides more comprehensive diagnostic information. This constitutes the online utilization of the KG. The latter realizes the offline use of the KG, providing users with a natural and user-friendly manner to retrieve fault diagnosis information. We demonstrate and verify our proposed framework in the context of the bearing fault diagnosis.

Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.

Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT. We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source. Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content. We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929. Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression.

Interactive Table Synthesis With Natural Language.

Tables are a ubiquitous data format for insight communication. However, transforming data into consumable tabular views remains a challenging and time-consuming task. To lower the barrier of such a task, research efforts have been devoted to developing interactive approaches for data transformation, but many approaches still presume that their users have considerable knowledge of various data transformation concepts and functions. In this study, we leverage natural language (NL) as the primary interaction modality to improve the accessibility of average users to performing complex data transformation and facilitate intuitive table generation and editing. Designing an NL-driven data transformation approach introduces two challenges: 1) NL-driven synthesis of interpretable pipelines and 2) incremental refinement of synthesized tables. To address these challenges, we present NL2Rigel, an interactive tool that assists users in synthesizing and improving tables from semi-structured text with NL instructions. Based on a large language model and prompting techniques, NL2Rigel can interpret the given NL instructions into a table synthesis pipeline corresponding to Rigel specifications, a declarative language for tabular data transformation. An intuitive interface is designed to visualize the synthesis pipeline and the generated tables, helping users understand the transformation process and refine the results efficiently with targeted NL instructions. The comprehensiveness of NL2Rigel is demonstrated with an example gallery, and we further confirmed NL2Rigel's usability with a comparative user study by showing that the task completion time with NL2Rigel is significantly shorter than that with the original version of Rigel with comparable completion rates.

A real-world data analysis of electronic health records to investigate the associations of predominant negative symptoms with healthcare resource utilisation, costs and treatment patterns among patients with schizophrenia

ObjectivesNegative symptoms in schizophrenia are associated with significant illness burden. We sought to investigate clinical outcomes for patients with schizophrenia who present with predominant negative symptoms (PNS) vs without PNS.DesignRetrospective...

Knowledge graph-derived feed efficiency analysis via pig gut microbiota

Feed efficiency (FE) is essential for pig production, has been reported to be partially explained by gut microbiota. Despite an extensive body of research literature to this topic, studies regarding the regulation of feed efficiency by gut microbiota remain fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Meanwhile, structured databases for microbiota analysis are available, yet they often lack a comprehensive understanding of the associated biological processes. Therefore, we have devised an approach to construct a comprehensive knowledge graph by combining unstructured textual intelligence with structured database information and applied it to investigate the relationship between pig gut microbes and FE. Firstly, we created the pgmReading knowledge base and the domain ontology of pig gut microbiota by annotating, extracting, and integrating semantic information from 157 scientific publications. Secondly, we created the pgmPubtator by utilizing PubTator to expand the semantic information related to microbiota. Thirdly, we created the pgmDatabase by mapping and combining the ADDAGMA, gutMGene, and KEGG databases based on the ontology. These three knowledge bases were integrated to form the Pig Gut Microbial Knowledge Graph (PGMKG). Additionally, we created five biological query cases to validate the performance of PGMKG. These cases not only allow us to identify microbes with the most significant impact on FE but also provide insights into the metabolites produced by these microbes and the associated metabolic pathways. This study introduces PGMKG, mapping key microbes in pig feed efficiency and guiding microbiota-targeted optimization.

A hybrid deep semantic mining method considering fuzzy expressions for the automatic recognition of construction safety hazard information

Safety hazards are a key consideration in construction management. The efficient recognition of safety hazard information can help managers formulate safety hazard management measures and improve the efficiency of construction safety management. However, construction site safety hazard data are stored in semistructured and unstructured text formats, which cannot be directly converted into understandable and usable information. Moreover, safety hazard text contains many fuzzy expressions, thereby increasing the difficulty of text semantic analysis; thus, how to accurately mine safety hazard information from complex and diverse text data is an urgent problem that must be solved. In consideration of this problem, we propose a bidirectional long short-term memory (BiLSTM) method with a fuzzy word vector and self-attention mechanism (FSABiLSTM) to automatically recognize safety hazard information. This method adopts TextRank and Word2vec to calculate the fuzzy word vector and process fuzzy expressions in safety hazard text. The safety hazard text semantic features are deeply extracted based on BiLSTM and a fuzzy word vector, and the extracted semantic features are analyzed via a self-attention mechanism. Actual construction safety hazard text is used to verify the reliability and applicability of the method, and the results indicate that the accuracy of this method, which outperforms existing machine learning methods, is 91.70%. In addition, the FSABiLSTM method can be used to automatically evaluate the risk degree of safety hazards; this use is beneficial to managing and controlling safety hazards. Concerning safety hazard text data, this study provides a new deep mining approach that can enhance safety management efficiency.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions

IntroductionPatients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients’ functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. MethodsFedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. ResultsADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. ConclusionNLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

Extracting named entities from Russian-language documents with different expressiveness of structure

This work is devoted to solving the problem of recognizing named entities for Russian-language texts based on the CRF model. Two sets of data were considered: documents on refinancing with a good document structure, semi-structured texts of court records. The model was tested under various sets of text features and CRF parameters (optimization algorithms). In average for all entities, the best F-measure value for structured documents was 0.99, and for semi-structured ones 0.86.

Development and Evaluation of a Natural Language Processing System for Curating a Trans-Thoracic Echocardiogram (TTE) Database.

Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse, because of their narrative structure. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. To develop an NLP-based approach for extracting and categorising data from echocardiography reports by accurately converting continuous (e.g., LVOT VTI, AV VTI and TR Vmax) and discrete (e.g., regurgitation severity) outcomes in a semi-structured narrative format into a structured and categorised format, allowing for future research or clinical use. 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and was iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Continuous outcomes including LVOT VTI, AV VTI and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC = 1.00, p < 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. A good level (ICC = 0.75-0.9, p < 0.05) of inter-rater reliability was observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to a structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

Applying Natural Language Processing to ClinicalTrials.gov: mRNA cancer vaccine case study.

Recently, biotechnology and pharmaceutical industries have made strides to adopt and implement Natural Language Processing (NLP) to address challenges faced when extracting and synthesizing high volumes of information found in unstructured and semistructured text. Here we present, and provide a summary of the findings from, a use case where NLP and text mining methodologies were used to extract clinical trial data from ClinicalTrials.gov for mRNA cancer vaccines.

MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions.

The microbiota-gut-brain axis denotes a two-way system of interactions between the gut and the brain, comprising three key components: (1) gut microbiota, (2) intermediates and (3) mental ailments. These constituents communicate with one another to induce changes in the host's mood, cognition and demeanor. Knowledge concerning the regulation of the host central nervous system by gut microbiota is fragmented and mostly confined to disorganized or semi-structured unrestricted texts. Such a format hinders the exploration and comprehension of unknown territories or the further advancement of artificial intelligence systems. Hence, we collated crucial information by scrutinizing an extensive body of literature, amalgamated the extant knowledge of the microbiota-gut-brain axis and depicted it in the form of a knowledge graph named MMiKG, which can be visualized on the GraphXR platform and the Neo4j database, correspondingly. By merging various associated resources and deducing prospective connections between gut microbiota and the central nervous system through MMiKG, users can acquire a more comprehensive perception of the pathogenesis of mental disorders and generate novel insights for advancing therapeutic measures. As a free and open-source platform, MMiKG can be accessed at http://yangbiolab.cn:8501/ with no login requirement.

Toward AI-supported evaluation for safety control measures against near-miss events in pharmaceutical products

The Japan Council for Quality Health Care (JCQHC) promotes medical safety by providing health professionals in pharmacies and the public with information regarding near-miss events from pharmacies. The Pharmaceuticals and Medical Devices Agency (PMDA) evaluates the pharmaceutical near-miss events published by the JCQHC to determine whether safety measures need to be taken in terms of drug names and packaging. We propose an artificial intelligence (AI) evaluation model that performs efficient and reproducible evaluations to support PMDA reviewers. We prepared a dataset consisting of pairs of pharmaceutical near-miss events and their human-annotated evaluation in consultation with the PMDA Safe Use Measures Review Committee reports. Pharmaceutical near-miss events consist of semi-structured texts such as drug names and free descriptive comments. To extract text features such as the similarity of drug names from the semi-structured text and predict the evaluation from these text features, a light gradient boosting machine was used. The AI model was evaluated by ablation experiments on these features. Model construction using the metrics of precision, recall, f1-score, and macro-f1 and verification were performed using cross-validation. The developed AI model classified events with a high degree of accuracy close to that of PMDA's human evaluation; however, achieving human classification accuracy proved difficult. Further study is needed to improve the accuracy of the classification evaluation model. The developed model should serve as an operational rule for primary screening in PMDA operations.

Automatic transparency evaluation for open knowledge extraction systems

BackgroundThis paper proposes Cyrus, a new transparency evaluation framework, for Open Knowledge Extraction (OKE) systems. Cyrus is based on the state-of-the-art transparency models and linked data quality assessment dimensions. It brings together a comprehensive view of transparency dimensions for OKE systems. The Cyrus framework is used to evaluate the transparency of three linked datasets, which are built from the same corpus by three state-of-the-art OKE systems. The evaluation is automatically performed using a combination of three state-of-the-art FAIRness (Findability, Accessibility, Interoperability, Reusability) assessment tools and a linked data quality evaluation framework, called Luzzu. This evaluation includes six Cyrus data transparency dimensions for which existing assessment tools could be identified. OKE systems extract structured knowledge from unstructured or semi-structured text in the form of linked data. These systems are fundamental components of advanced knowledge services. However, due to the lack of a transparency framework for OKE, most OKE systems are not transparent. This means that their processes and outcomes are not understandable and interpretable. A comprehensive framework sheds light on different aspects of transparency, allows comparison between the transparency of different systems by supporting the development of transparency scores, gives insight into the transparency weaknesses of the system, and ways to improve them. Automatic transparency evaluation helps with scalability and facilitates transparency assessment. The transparency problem has been identified as critical by the European Union Trustworthy Artificial Intelligence (AI) guidelines. In this paper, Cyrus provides the first comprehensive view of transparency dimensions for OKE systems by merging the perspectives of the FAccT (Fairness, Accountability, and Transparency), FAIR, and linked data quality research communities.ResultsIn Cyrus, data transparency includes ten dimensions which are grouped in two categories. In this paper, six of these dimensions, i.e., provenance, interpretability, understandability, licensing, availability, interlinking have been evaluated automatically for three state-of-the-art OKE systems, using the state-of-the-art metrics and tools. Covid-on-the-Web is identified to have the highest mean transparency.ConclusionsThis is the first research to study the transparency of OKE systems that provides a comprehensive set of transparency dimensions spanning ethics, trustworthy AI, and data quality approaches to transparency. It also demonstrates how to perform automated transparency evaluation that combines existing FAIRness and linked data quality assessment tools for the first time. We show that state-of-the-art OKE systems vary in the transparency of the linked data generated and that these differences can be automatically quantified leading to potential applications in trustworthy AI, compliance, data protection, data governance, and future OKE system design and testing.

진로전환 경험이 있는 상담교사의 성장에 관한 내러티브 탐구

This study aims to investigate the story about the force in career transition of school counselors and about the growth as school counselors after career transition. For this purpose, three elementary school counselors with experience as part-time subject teachers participated, the data were collected by semi-structured interview, cellphone, text message, and e-mail for about 5 months. The stories were presented in order of experience over time. This study adopted the stage of ‘being in the field, from field to field text, composing field text, from field text to research text, composing research text’. And the career transition process and occupational adaptation process as a school counselors were analyzed according to the narrative inquiry procedure. These stories consisted of the experiences before career transition, and the occupational experiences and the growth after transition. First, the story about the force in career transition showed participants expeirenced the limitations in reality and the needs to become a meaninful person. Their desire to help the students triggered the career transition, and there were the happenstances leading to their positive choices. Second, the living stories as school counselors were composed of the confusion and the struggling in new culture and system owing to career transition. They experienced the emotional exhaustion and difficulties due to unfamiliar school culture, confusion due to ambiguous roles, and lack of awareness of counseling. Third, in the stories of their growth, a lot of efforts to establish the identity by using the previous experiences before transition, forming a cooperative relationship with the educational community, and expanding themselves and professionalism were embroidered. I discussed these stories in the context of personal, practical, and social legitimacy to expand the meaning of these narrative.

A survey on Relation Extraction

With the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly. This unstructured or semi-structured text contains a huge amount of information. Information Extraction (IE) can extract meaningful information from text sources and present it in a structured format. The sub-tasks of IE include Named Entity Recognition (NER), Event Extraction, Relation Extraction (RE), Sentiment Extraction, Opinion Extraction, Terminology Extraction, Reference Extraction, and so on.One way to represent information in the text is in the form of entities and relations representing links between entities. The Entity Extraction task identifies entities from the text, and the Relation Extraction (RE) task can identify relationships between those entities. Many NLP applications can benefit from relational information derived from natural language, including Structured Search, Knowledge Base (KB) population, Information Retrieval, Question-Answering, Language Understanding, Ontology Learning, etc. This survey covers (1) basic concepts of Relation Extraction; (2) various Relation Extraction methodologies; (3) Deep Learning techniques for Relation Extraction; and (4) different datasets that can be used to evaluate the RE system.

The Medium Is the Definer: Daily Journalism as a Tool for Forming Community: A Case Study—The Ultra-Orthodox Community in Israel

There are many studies that deal with the role of media and the motives for their creation. The present article explores the background behind the development of Ultra-Orthodox journalism. It examines the establishment of Ultra-Orthodox daily newspapers in Israel at the end of the twentieth century and the beginning of the twenty-first by analyzing semistructured texts and ideas. The historical background and the way this journalism developed in general and specifically during those years reflect a strong social and censorial orientation. This study concludes that these newspapers can be seen as social definers that help preserve a community through censorship aimed at the general and other Ultra-Orthodox media. It helps communities and individuals self-define and can delineate an additional role for mass media as a social definer. The contention herein is that the establishment of Ultra-Orthodox newspapers in Israel serves as a mode for social definition; a definition that is arrived at by being part of the circle of the newspaper’s readers, and the newspaper, in turn, defines itself by its censor board.

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5min. We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.

Exploring the development of Islamic fintech ecosystem in Indonesia: a text analytics

Purpose This study aims at two objectives, i.e. first, to identify the core elements of the Islamic fintech ecosystem, and second, to use the identified core elements to analyse the development of such an ecosystem in Indonesia. Design/methodology/approach This work combines data analytics of text mining with qualitative analysis of human intelligence in two steps. First, knowledge discovery of the Islamic fintech ecosystem’s core elements using a sample of eight academic articles totalling 102 pages and 75,082 words. Second, using the identified core elements from step one to explore such ecosystem development in Indonesia. This stage employs a sample of 11 documents totalling 371 pages and 143,032 words from cyberspace. Findings The core elements of the Islamic fintech ecosystem identified are financial customers, fintech startups, government, technology developers, traditional financial institutions and fatwa (Islamic legal opinion). Furthermore, the development of the Islamic fintech ecosystem in Indonesia is examined under these identified core elements, providing critical insights into the Islamic fintech ecosystem currently established in the country's industry. Research limitations/implications This study primarily used semi-structured data from cyberspace. Traditional approaches to qualitative data collection, e.g. focused group discussions and interviews, may be beneficial for future studies in addressing the Islamic fintech ecosystem issues. Practical implications Academia worldwide may benefit from this work in incorporating knowledge of Islamic fintech ecosystem’s core elements into Islamic finance literature. Specifically, fintech stakeholders in Indonesia may be advantaged to understand how far the Islamic fintech ecosystem has grown in the country. Social implications The rise of unethical fintech peer-to-peer lending shows social problems in Indonesia’s fintech industry. The finding derives social implications that elucidate the current state of the country’s Islamic fintech ecosystem. Originality/value Using a kind of big data (i.e. semi-structured text data) from cyberspace and applying steps of text mining combined with qualitative analysis, may contribute to the creation of novelties for qualitative research on financial issues.

Application of recommendation system in educational Field

The article is devoted to the issue of the application of recommender system technology in the educational sphere. It was determined that certain tasks of the educational field can be described in terms of recommendation systems: the user who is given a recommendation is the education applicant; the objects of his interest are disciplines from the catalog of optional disciplines, sources from the library of the educational institution, topics (or research area) of qualification papers, potential supervisors of qualification papers, external courses for the organization of self-education. A list of resources was created to determine the characteristics of the education applicants’ objects of interest and the education applicants themselves within the framework of formal education, and an analysis of their content and method of formation was carried out. The structure of the recommendation system has been developed; its feature is that each object of interest of the education applicant corresponds to a separate module of recommendations. At the same time, the recommendation modules use shared resources, including databases of the educational institution with direct information about the objects of interest and the education applicant, as well as survey databases consisting of general regular mandatory surveys for education applicants at the level of the educational institution and contextual surveys regarding objects of interest. A hybrid approach was chosen to form the recommendations. The structure of the system provides for the use of collaborative filtering for processing survey results, and data presented in the form of semi-structured or arbitrary texts in natural language is proposed to be processed using Natural Language Processing and Text Mining methods, which will allow taking into account semantically similar values of the characteristics of objects of interest and users and thus to improve the quality of the recommendation system.

ТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ И СУММАРИЗАЦИЯ ТЕКСТОВ В ОБЛАСТИ КИБЕРБЕЗОПАСНОСТИ

Purpose: improving the quality of text document analysis through the use of machine learning and intellectual analysis models in summarizing and topic modeling tasks, which will reduce the load on an expert who analyzes and generalizes significant volumes of semi-structured text data on information security topics from various sources. Methods: machine learning methods were used for the operational processing and analysis of large volumes of heterogeneous ill-structured information in natural language (NL). Methods of thematic modeling and summarization of texts based on deep neural networks, including neural network language models based on the architecture of transformers, are applied. Practical relevance: the main stages of the machine procedure of thematic modeling and summarization of professional texts in the field of information security are highlighted. The results of a comparative evaluation of the effectiveness of using clustering models, latent semantic analysis, Fast Text, Text Rank language models and BERT transformers for these purposes are presented. Recommendations are given regarding the prospects for the practical application of these models as a means of intellectual support for the professional activities of cybersecurity specialists. Scientific novelty: a complex of machine learning models for thematic modeling and summarization of professional texts is proposed, based on neural network models of attachments and transformer models, characterized by an algorithm for preparing a corpus of texts for training models and the use of a learning transfer algorithm, which will increase the efficiency of analysis and generalization of domain-specific corpora of texts.