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Journal articles on the topic 'Representation Learning; Author and Document'

Author: Grafiati

Published: 22 June 2024

Last updated: 31 July 2025

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1

Para, Upendar, and M. S. Patel. "A New Term Representation Method for Gender and Age Prediction." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 90–104. http://dx.doi.org/10.17762/ijritcc.v11i5s.6633.

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Author Profiling is a kind of text classification method that is used for detecting the personality profiles such as age, gender, educational background, place of origin, personality traits, native language, etc., of authors by processing their written texts. Several applications like forensic analysis, security and marking are used the techniques of author profiling for finding the basic details of authors. The main problem in the domain of author profiling is preparation of suitable dataset for predicting the characteristics of authors. PAN is one organization conducting competitions on vari

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Ma, Yingying, Youlong Wu, and Chengqiang Lu. "A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory." Entropy 22, no. 4 (2020): 416. http://dx.doi.org/10.3390/e22040416.

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Name ambiguity, due to the fact that many people share an identical name, often deteriorates the performance of information integration, document retrieval and web search. In academic data analysis, author name ambiguity usually decreases the analysis performance. To solve this problem, an author name disambiguation task is designed to divide documents related to an author name reference into several parts and each part is associated with a real-life person. Existing methods usually use either attributes of documents or relationships between documents and co-authors. However, methods of featur

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Pooja, Km, Samrat Mondal, and Joydeep Chandra. "Exploiting Higher Order Multi-dimensional Relationships with Self-attention for Author Name Disambiguation." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (2022): 1–23. http://dx.doi.org/10.1145/3502730.

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Name ambiguity is a prevalent problem in scholarly publications due to the unprecedented growth of digital libraries and number of researchers. An author is identified by their name in the absence of a unique identifier. The documents of an author are mistakenly assigned due to underlying ambiguity, which may lead to an improper assessment of the author. Various efforts have been made in the literature to solve the name disambiguation problem with supervised and unsupervised approaches. The unsupervised approaches for author name disambiguation are preferred due to the availability of a large

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Stoean, Catalin, and Daniel Lichtblau. "Author Identification Using Chaos Game Representation and Deep Learning." Mathematics 8, no. 11 (2020): 1933. http://dx.doi.org/10.3390/math8111933.

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An author unconsciously encodes in the written text a certain style that is often difficult to recognize. Still, there are many computational means developed for this purpose that take into account various features, from lexical and character-based attributes to syntactic or semantic ones. We propose an approach that starts from the character level and uses chaos game representation to illustrate documents like images which are subsequently classified by a deep learning algorithm. The experiments are made on three data sets and the outputs are comparable to the results from the literature. The

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Kavuri, Karunakar, and M. Kavitha. "A Word Embeddings based Approach for Author Profiling: Gender and Age Prediction." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 239–50. http://dx.doi.org/10.17762/ijritcc.v11i7s.6996.

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Author Profiling (AP) is a method of identifying the demographic profiles such as age, gender, location, native language and personality traits of an author by processing their written texts. The AP techniques are used in multiple applications such as literary research, marketing, forensics and security. The researchers identified various differences in the authors writing styles by analysing various datasets. The differences in writing styles are represented as stylistic features. The researchers extracted several style based features like structural, content, word, character, syntactic, read

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Buffone, Brittany, Ilena Djuana, Katherine Yang, Kyle J. Wilby, Maguy S. El Hajj, and Kerry Wilbur. "Diversity in health professional education scholarship: a document analysis of international author representation in leading journals." BMJ Open 10, no. 11 (2020): e043970. http://dx.doi.org/10.1136/bmjopen-2020-043970.

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ObjectivesThe global distribution of health professionals and associated training programmes is wide but prior study has demonstrated reported scholarship of teaching and learning arises from predominantly Western perspectives.DesignWe conducted a document analysis to examine authorship of recent publications to explore current international representation.Data sourcesThe table of contents of seven high-impact English-language health professional education journals between 2008 and 2018 was extracted from Embase.Eligibility criteriaThe journals were selected according to highest aggregate rank

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Sunil D. Kale. "Deep Learning-Based Hybridized Bi-LSTM Model for Author Identification in the Marathi Language." Advances in Nonlinear Variational Inequalities 28, no. 4s (2025): 01–22. https://doi.org/10.52783/anvi.v28.3222.

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Nowadays, author identification system for linguistics articles has imperatively needed for copy right violations. It is the activity in which linguistic attempts to identify the original author of an unknown textual information based on the utilized vocabulary and the writing style of the author. There have a lot of existing studies focusing on popular languages like English, Spanish, Chinese, and so on. In this paper, we propose an author identification system for Indian language Marathi based on hybridized Bi-directional long short-term memory (Bi-LSTM) model. The Marathi language totally d

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Popova, Y. B., and A. V. Goloburda. "ALGORITHMIC AND PROGRAM IMPLEMENTATION OF THE PLAGIARISM DEFINITION IN LEARNING MANAGEMENT SYSTEMS." «System analysis and applied information science», no. 1 (June 12, 2018): 71–78. http://dx.doi.org/10.21122/2309-4923-2018-1-71-78.

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The main advantage of using information technologies in education, which consists in speeding up and simplifying of information exchange, is also its drawback, because it raises the problem of plagiarism. The purpose of this paper is to develop testing text software for uniqueness in learning management systems. To achieve this goal, it is necessary to solve a range of problems related to the choice of a method for determining plagiarism, its algorithmization and software implementation. The work deals with the methods of shingles, super-shingles, signature methods, vector models of text repre

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Popova, Oleksandra. "ECONOMIC AND LEGAL DISCOURSE: PARADIGM OF CHANGES IN THE XXI CENTURY (ON THE MATERIAL OF CHINESE, ENGLISH AND UKRAINIAN LANGUAGES)." Naukovy Visnyk of South Ukrainian National Pedagogical University named after K. D. Ushynsky: Linguistic Sciences 2022, no. 34 (2022): 61–73. http://dx.doi.org/10.24195/2616-5317-2022-34-6.

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The article is devoted to the study of the paradigm of changes in the content of official documents regulating economic and legal relations in the academic sphere in the XXI century. The author considers the factors influencing the process of changes in the content of official documents regulating economic and legal relations in the academic sphere in the context of the linguistic-translation paradigm. The concepts “economic and legal discourse”, “composition of the text of the document”, “academic sphere” have been clarified. Economic and legal discourse is determined through the prism of its

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Dalyan, Tuğba, Hakan Ayral, and Özgür Özdemir. "A Comprehensive Study of Learning Approaches for Author Gender Identification." Information Technology and Control 51, no. 3 (2022): 429–45. http://dx.doi.org/10.5755/j01.itc.51.3.29907.

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In recent years, author gender identification is an important yet challenging task in the fields of information retrieval and computational linguistics. In this paper, different learning approaches are presented to address the problem of author gender identification for Turkish articles. First, several classification algorithms are applied to the list of representations based on different paradigms: fixed-length vector representations such as Stylometric Features (SF), Bag-of-Words (BoW) and distributed word/document embeddings such as Word2vec, fastText and Doc2vec. Secondly, deep learning ar

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Surya, Chennam Chandrika, Karunakar K, Murali Mohan T, and R. Prasanthi Kumari. "Language Variety Prediction using Word Embeddings and Machine Leaning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1616–23. http://dx.doi.org/10.22214/ijraset.2022.48280.

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Abstract: Author Profiling is a technique of predicting demographic characteristics like gender, age, location, nativity language, educational background etc., of an author by analysing their written texts. Author profiling is used in several text processing applications like forensics analysis, marketing, security. The author profiling techniques identify the stylistic differences among the author writing styles to identify the demographics of authors. Researchers experimented with various stylistic features like lexical features, content-based features, syntactic features, semantic features,

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Tarmizi, Nursyahirah, Suhaila Saee, and Dayang Hanani Abang Ibrahim. "TOWARDS CURBING CYBER-BULLYING IN MALAYSIA BY AUTHOR IDENTIFICATION OF IBAN AND KADAZANDUSUN OSN TEXT USING DEEP LEARNING." ASEAN Engineering Journal 13, no. 2 (2023): 145–57. http://dx.doi.org/10.11113/aej.v13.19171.

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Online Social Network (OSN) is frequently used to carry out cyber-criminal actions such as cyberbullying. As a developing country in Asia that keeps abreast of ICT advancement, Malaysia is no exception when it comes to cyberbullying. Author Identification (AI) task plays a vital role in social media forensic investigation (SMF) to unveil the genuine identity of the offender by analysing the text written in OSN by the candidate culprits. Several challenges in AI dealing with OSN text, including limited text length and informal language full of internet jargon and grammatical errors that further

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Anwar, Waheed, Imran Sarwar Bajwa, and Shabana Ramzan. "Design and Implementation of a Machine Learning-Based Authorship Identification Model." Scientific Programming 2019 (January 16, 2019): 1–14. http://dx.doi.org/10.1155/2019/9431073.

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In this paper, a novel approach is presented for authorship identification in English and Urdu text using the LDA model with n-grams texts of authors and cosine similarity. The proposed approach uses similarity metrics to identify various learned representations of stylometric features and uses them to identify the writing style of a particular author. The proposed LDA-based approach emphasizes instance-based and profile-based classifications of an author’s text. Here, LDA suitably handles high-dimensional and sparse data by allowing more expressive representation of text. The presented approa

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Smriti Priya Medhi. "Ensemble Deep Learning for Author Attribution in Assamese Text Documents: A Hybrid Approach." Journal of Information Systems Engineering and Management 10, no. 40s (2025): 419–26. https://doi.org/10.52783/jisem.v10i40s.7312.

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Introduction: Author attribution is a critical task in the domain of computational linguistics, particularly when dealing with low-resourced languages. These languages often lack sufficient annotated datasets and robust linguistic tools, making natural language processing (NLP) applications challenging. A piece of text generally reflects the unique stylistic traits of its author, including their use of vocabulary, punctuation, and sentence structures. Identifying the correct author based on these textual cues is the central aim of author attribution. Despite its importance, this area remains r

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Zhao, Wentao, Dalin Zhou, Xinguo Qiu, and Wei Jiang. "How to Represent Paintings: A Painting Classification Using Artistic Comments." Sensors 21, no. 6 (2021): 1940. http://dx.doi.org/10.3390/s21061940.

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The goal of large-scale automatic paintings analysis is to classify and retrieve images using machine learning techniques. The traditional methods use computer vision techniques on paintings to enable computers to represent the art content. In this work, we propose using a graph convolutional network and artistic comments rather than the painting color to classify type, school, timeframe and author of the paintings by implementing natural language processing (NLP) techniques. First, we build a single artistic comment graph based on co-occurrence relations and document word relations and then t

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García-Gorrostieta, Jesús Miguel, Aurelio López-López, Samuel González-López, and Adrián Pastor López-Monroy. "Improved argumentative paragraphs detection in academic theses supported with unit segmentation." Journal of Intelligent & Fuzzy Systems 42, no. 5 (2022): 4481–91. http://dx.doi.org/10.3233/jifs-219237.

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Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to

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Lee, Seungpeel, Honggeun Ji, Jina Kim, and Eunil Park. "What books will be your bestseller? A machine learning approach with Amazon Kindle." Electronic Library 39, no. 1 (2021): 137–51. http://dx.doi.org/10.1108/el-08-2020-0234.

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Purpose With the rapid increase in internet use, most people tend to purchase books through online stores. Several such stores also provide book recommendations for buyer convenience, and both collaborative and content-based filtering approaches have been widely used for building these recommendation systems. However, both approaches have significant limitations, including cold start and data sparsity. To overcome these limitations, this study aims to investigate whether user satisfaction can be predicted based on easily accessible book descriptions. Design/methodology/approach The authors col

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Pogorilyy, S. D., A. A. Kramov, and P. V. Biletskyi. "METHOD FOR COHERECE EVALUATION OF UKRAINIAN TEXTS USING CONVO-LUTIONAL NEURAL NETWORK." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 65 (2019): 64–71. http://dx.doi.org/10.17721/2519-481x/2019/65-08.

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The estimation of text coherence is one of the most actual tasks of computer linguistics. Analysis of text coherence is widely used for writing and selection of documents. It allows clearly conveying the idea of an author to a reader. The importance of this task can be confirmed by the availability of actual works that are dedicated to solving it. Different automated methods for the estimation of text coherence are based on the methodology of machine learning. Corresponding methods are based on of formal text representation and following detection of regularities for the generation of an outpu

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Song, Kaisong, Yangyang Kang, Wei Gao, Zhe Gao, Changlong Sun, and Xiaozhong Liu. "Evidence Aware Neural Pornographic Text Identification for Child Protection." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 14939–47. http://dx.doi.org/10.1609/aaai.v35i17.17753.

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Identifying pornographic text online is practically useful to protect children from access to such adult content. However, some authors may intentionally avoid using sensitive words in their pornographic texts to take advantage of the lack of human audits. Without prior knowledge guidance, real semantics of such pornographic text is difficult to understand by existing methods due to its high context-sensitivity and heavy usage of figurative language, which brings huge challenges to the porn detection systems used in social media platforms. In this paper, we approach to the problem as a documen

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Polat, Huseyin, and Mesut Korpe. "Estimation of Demographic Traits of the Deputies through Parliamentary Debates Using Machine Learning." Electronics 11, no. 15 (2022): 2374. http://dx.doi.org/10.3390/electronics11152374.

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One of the most impressive applications of the combined use of natural language processing (NLP), classical machine learning, and deep learning (DL) approaches is the estimation of demographic traits from the text. Author Profiling (AP) is the analysis of a text to identify the demographics or characteristics of its author. So far, most researchers in this field have focused on using social media data in the English language. This article aims to expand the predictive potential of demographic traits by focusing on a more diverse dataset and language. Knowing the background of deputies is essen

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Utami, Putri Lintang, Nadi Suprapto, and Hasan Nuurul Hidaayatullaah. "Exploring Research Trends of Physics Concept Mapping in Physics Learning: Bibliometric Analysis." Studies in Philosophy of Science and Education 3, no. 2 (2022): 58–69. http://dx.doi.org/10.46627/sipose.v3i2.308.

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Research related to the application of concept mapping in physics learning is increasing and has become a trending topic for decades. This research purpose are (1) Analyze the research network and visualization concept mapping research in physics learning and contributions to learning physics (2) Analyze contribution of Indonesian Researchers on Concept mapping research (3) Analyze research recommendations related to concept mapping in physics learning. This bibliometric analysis using Scopus database in 2012-2023 period documents and analyze by VOSviewer. Visualization results with physics co

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Brown, John Seely. "Process versus Product: A Perspective on Tools for Communal and Informal Electronic Learning." Journal of Educational Computing Research 1, no. 2 (1985): 179–201. http://dx.doi.org/10.2190/l00t-22h0-b7nj-1324.

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This article explores new paradigms for the use of computers in learning. Two concepts crucial to the development of qualitatively new kinds of computer-based learning environments are identified: the importance of focusing on the underlying process rather than just the product of a creative effort; and the importance of the computer's ability to record, represent and communicate that underlying process. We discuss the cognitive, pedagogical, and sociological issues relevant to the creation of learning environments in five domains, along with examples of specific possibilities in each: 1) Empo

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Juanes, Juan A., and Pablo Ruisoto. "Technological Advances and Teaching Innovation Applied to Health Science Education." Journal of Information Technology Research 7, no. 2 (2014): 1–6. http://dx.doi.org/10.4018/jitr.2014040101.

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The objective of this special issue under the title “Technological advances and teaching innovation applied to health science education” is to improve health science education, to encourage the information exchange and dissemination regarding different training aspects in medical science. Technological procedures in teaching entail an important adequacy and teaching content analysis to transmit and be acquired by students, as well as their careful presentation so that the message and knowledge reach the student more effectively. Due to this, the design of technological applications is very imp

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Kadu, Mr Lukesh, Dr Manoj Deshpande, and Dr Vijaykumar Pawar. "Survey of Deep Learning Approaches for Twitter Text Classification." International Journal of Advanced Engineering Research and Science 9, no. 12 (2022): 106–12. http://dx.doi.org/10.22161/ijaers.912.12.

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Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains

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Abayeva, Galiya A., Gulzhan S. Orazayeva, Saltanat J. Omirbek, Gaukhar B. Ibatova, Venera G. Zakirova, and Vera K. Vlasova. "A cross-database bibliometric analysis of ubiquitous learning: Trends, influences, and future directions." Contemporary Educational Technology 15, no. 4 (2023): ep471. http://dx.doi.org/10.30935/cedtech/13648.

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The concept of ubiquitous learning has emerged as a pedagogical approach in response to the advancements made in mobile, wireless communication, and sensing technologies. The domain of ubiquitous learning is distinguished by swift progression, thereby presenting a difficulty in maintaining current knowledge of its developments. The implementation of bibliometric analysis would enable the tracking of its development and current status. The objective of the present investigation is to perform a thorough bibliometric examination of the domain of ubiquitous learning. This research aims to discern

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Andriani, Agis, Fuad Abdullah, Enjang Nurhaedin, Arini Nurul Hidayati, Dewi Rosmala, and Yuyus Saputra. "The Representation of Counterproductive Religious Values in a Selected Chapter of an Indonesian ELT Textbook: Systemic Functional Multimodal Discourse Analysis." Journal of Pragmatics and Discourse Research 4, no. 1 (2024): 47–62. http://dx.doi.org/10.51817/jpdr.v4i1.756.

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Countless studies have examined the vital role of ELT textbooks as learning sources, particularly in terms of intercultural, multicultural, and trans-cultural analysis. Yet, none of them specifically talked about religious values as the research focus. Hence, this study aimed at construing religious values represented in a selected chapter of an Indonesian ELT textbook. Descriptive problem-driven content analysis was used as the research design, whilst the research data were collected through document analysis. Later, to analyze the data, the research utilized Systemic Functional Multimodal Di

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Popova, Oleksandra V. "ECONOMIC AND LEGAL DOCUMENTS IN THE ACADEMIC SPHERE: SYNERGISM OF TRANSLATION (ON THE MATERIAL OF ENGLISH, CHINESE AND UKRAINIAN)." Мова, no. 41 (May 28, 2024): 28–37. http://dx.doi.org/10.18524/2307-4558.2024.41.311207.

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The purpose of the article is to analyze English and Chinese texts of the authentic official documents regulating economic and legal relations in the academic sphere in the XXI century in the context of their translation into Ukrainian. The object of the study is the foreign documentary support of the process of Ukraine’s EU integration and cooperation with the P.R.C. within the academic environment. The subject of the research is the reproduction tools of the official documents regulating cooperation terms and conditions between Ukraine, European Union, and China in the academic sphere from E

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Liu, Yi, Qiuyan Zhu, Pengfei Jiang, et al. "Bibliometric and visualized analysis of DME from 2012 to 2022." Medicine 103, no. 13 (2024): e37347. http://dx.doi.org/10.1097/md.0000000000037347.

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Background: Diabetic macular edema (DME) is the main cause of irreversible vision loss in patients with diabetes mellitus (DM), resulting in a certain burden to patients and society. With the increasing incidence of DME, more and more researchers are focusing on it. Methods: The papers related to DME between 2012 and 2022 from the Web of Science core Collection were searched in this study. Based on CiteSpace and VOS viewer, these publications were analyzed in terms of spatiotemporal distribution, author distribution, subject classification, topic distribution, and citations. Results: A total o

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Pappas, Nikolaos, and Andrei Popescu-Belis. "Explicit Document Modeling through Weighted Multiple-Instance Learning." Journal of Artificial Intelligence Research 58 (March 22, 2017): 591–626. http://dx.doi.org/10.1613/jair.5240.

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Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) fra

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Pappas, Nikolaos, and Andrei Popescu-Belis. "Explicit Document Modeling through Weighted Multiple-Instance Learning." Journal of Artificial Intelligence Research 50 (2017) (March 1, 2017): 591–626. https://doi.org/10.1613/jair.5240.

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Representing documents is a crucial component in many NLP tasks, for instance predicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with generally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Derived from the weighted multiple-instance regression (MIR) fra

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Macri, Carmelo, Stephen Bacchi, Sheng Chieh Teoh, et al. "Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis." Journal of Medical Internet Research 25 (March 7, 2023): e42789. http://dx.doi.org/10.2196/42789.

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Background Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. Methods PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms “ophthalmology,” “radiology,” and

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Minang, Putri Sundari, and Gustaman Saragih. "LEXICAL COLLOCATION AND TEXTUAL COHESION IN STUDENT’S ENGLISH WRITING DISCUSSION TEXT." INFERENCE: Journal of English Language Teaching 5, no. 1 (2022): 62. http://dx.doi.org/10.30998/inference.v5i1.8068.

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<p><strong>Abstract</strong><strong>: </strong>The purpose of this study was to determine (1) the form of significant lexical collocation in student's English writing discussion text, (2) the process of lexical collocation creates cohesion in student's English writing discussion text, and (3) the cultural reflection displayed by the structure of lexical collocation and textual cohesion in student's English writing discussion text. This study applies a mixture of descriptive and quantitative qualitative methods in describing research findings. The data source of th

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ANASTASIU, Mădălina, and Răzvan Adrian CĂLUGĂREANU. "DESIGN OF LEARNING UNITS FOR SCHOOL PHYSICAL EDUCATION WITH 6TH GRADE STUDENTS." UNIVERSITY ARENA 1, no. 1/2018 (2018): 13–17. https://doi.org/10.62229/uai_1_18-2.

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Background. From the unitary documents of the teacher of physical education, the didactic design acquires a personalized and individual character, regarding the distribution of the learning units in lessons and throughout the school year, the joining of the learning units into lessons, the placement of the test events for the sunmative evaluation and of the tests stipulated by the National School System of Evaluation, as well as those established by the teacher in the structure of the school year. Objectives. This research aimed at the following objectives: to present the theoretical documenta

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Karunakar Kavuri,. "A Novel Document Representation Method for Author Profiling using Auto-Encoders." Journal of Information Systems Engineering and Management 10, no. 11s (2025): 516–29. https://doi.org/10.52783/jisem.v10i11s.1648.

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Author profiling is used for identifying certain demographic characteristics including age, gender, religion, language, nationality, and others of an author for a certain text. Author profiling has applications in different areas such as marketing, security, education, and forensics. Most of the research works concentrated on predicting the age and gender of an author by analyzing the writings of authors. The researchers started research on author profiling by using different types of stylistic features. Later, they realized that same set of stylistic features are used by the different classes

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Yan, Yan, Xu-Cheng Yin, Sujian Li, Mingyuan Yang, and Hong-Wei Hao. "Learning Document Semantic Representation with Hybrid Deep Belief Network." Computational Intelligence and Neuroscience 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/650527.

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High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each

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Siddiqui, Shoaib Ahmed, Andreas Dengel, and Sheraz Ahmed. "Self-Supervised Representation Learning for Document Image Classification." IEEE Access 9 (2021): 164358–67. http://dx.doi.org/10.1109/access.2021.3133200.

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Zhang, Wenyue, Yang Li, and Suge Wang. "Learning document representation via topic-enhanced LSTM model." Knowledge-Based Systems 174 (June 2019): 194–204. http://dx.doi.org/10.1016/j.knosys.2019.03.007.

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Liu, Ying, Tongzhou Zhao, Yue Chai, and Yiqi Jiang. "A Word Elimination Strategy for Learning Document Representation." IOP Conference Series: Materials Science and Engineering 466 (December 28, 2018): 012091. http://dx.doi.org/10.1088/1757-899x/466/1/012091.

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Buckley, Charles, and Chrissi Nerantzi. "Effective Use of Visual Representation in Research and Teaching within Higher Education." International Journal of Management and Applied Research 7, no. 3 (2020): 196–214. https://doi.org/10.18646/2056.73.20-014.

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There are now increasing opportunities for educators to use creative forms of visual representation in their professional practice. Despite the potential for increasing researcher and teacher understanding and student engagement and learning through the proliferation of visual material, the rationale and deliberate planning of using images remains relatively unexplored. The potential benefits to learners through the incorporation of visual representation on its own or with text are well-documented although the ways in which it can be used effectively is less well-established. This paper provid

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Takale, Sheetal Ajaykumar. "Knowledge Representation for Legal Document Summarization." International Journal of Innovative Research in Computer Science and Technology 11, no. 4 (2023): 61–66. http://dx.doi.org/10.55524/ijircst.2023.11.4.11.

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This paper presents a novel approach for legal document summarization. Proposed approach is based on Ripple-Down Rules (RDR). It is an incremental knowledge acquisition method. RDR allows us to quickly build an extendable knowledge base using classification rules. The classification rules are written using a set of features. Summary is generated using the identified rhetorical roles in the document. Experiments demonstrate that the RDR based Legal Document summarization approach outperforms the supervised and unsupervised machine learning models.

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Sheetal, Ajaykumar Takale. "Knowledge Representation for Legal Document Summarization." International Journal of Innovative Research in Computer Science and Technology (IJIRCST) 11, no. 04 (2023): 61–66. https://doi.org/10.55524/ijircst.2023.11.4.11.

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This paper presents a novel approach for legal document summarization. Proposed approach is based on Ripple-Down Rules (RDR). It is an incremental knowledge acquisition method. RDR allows us to quickly build an extendable knowledge base using classification rules. The classification rules are written using a set of features. Summary is generated using the identified rhetorical roles in the document. Experiments demonstrate that the RDR based Legal Document summarization approach outperforms the supervised and unsupervised machine learning models.

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Morariu, Daniel, Lucian Vințan, and Radu Crețulescu. "An Extension of the VSM Documents Representation using Word Embedding." Balkan Region Conference on Engineering and Business Education 2, no. 1 (2017): 249–57. http://dx.doi.org/10.1515/cplbu-2017-0033.

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Abstract In this paper, we will present experiments that try to integrate the power of Word Embedding representation in real problems for documents classification. Word Embedding is a new tendency used in the natural language processing domain that tries to represent each word from the document in a vector format. This representation embeds the semantically context in that the word occurs more frequently. We include this new representation in a classical VSM document representation and evaluate it using a learning algorithm based on the Support Vector Machine. This new added information makes

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Younas, Junaid, Shoaib Ahmed Siddiqui, Mohsin Munir, et al. "Fi-Fo Detector: Figure and Formula Detection Using Deformable Networks." Applied Sciences 10, no. 18 (2020): 6460. http://dx.doi.org/10.3390/app10186460.

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We propose a novel hybrid approach that fuses traditional computer vision techniques with deep learning models to detect figures and formulas from document images. The proposed approach first fuses the different computer vision based image representations, i.e., color transform, connected component analysis, and distance transform, termed as Fi-Fo image representation. The Fi-Fo image representation is then fed to deep models for further refined representation-learning for detecting figures and formulas from document images. The proposed approach is evaluated on a publicly available ICDAR-2017

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Daele, Amaury. "Reifying, Participating and Learning." International Journal of Web-Based Learning and Teaching Technologies 5, no. 1 (2010): 43–60. http://dx.doi.org/10.4018/jwltt.2010010104.

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This paper presents observations and analysis of an activity of reification of professional practices within a community of practice. A case is examined of a distance community of tutors using a semantic Wiki for formalising their practices and a tool for storing and classifying documents. On the basis of the instrumental genesis theory, the author highlights the process of appropriation of the tools by the community of practice. This community participated in the development and conception of uses for the tools through a research and development project based on participatory design. This app

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Krisnaningsih, Erina, Maharani Ayu Nurdiana Putri, Tsabitamia Irba, Nadi Supapto, Utama Alan Deta, and Eko Hariyono. "Bibliometric Analysis of Multi Representation Based on Problem-Solving Skills Using VOSviewer." Berkala Ilmiah Pendidikan Fisika 9, no. 3 (2021): 274. http://dx.doi.org/10.20527/bipf.v9i3.11329.

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The purpose of this study was to analyze the scope related to the subject of problem-solving skills based on multiple representation in 2016 – 2020 with 20 documents through bibliometric analysis. The research method used was a literature study through all the articles analyzed in this study. The articles were taken from the Scopus database with sampling in 2003 – 2020, resulting in 29 scientific work data exported in *.ris (RIS) and *CVS formats. Then, those data were processed using VOSviewer and Microsoft Excel. The results of publications in the last five years have increased. Indonesia is

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Kilimci, Zeynep H., and Selim Akyokus. "Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification." Complexity 2018 (October 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7130146.

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The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with

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Stolyarov, Yu N. "Theory for the good of practice. To discussion on the origins of documentology." Scientific and Technical Libraries, no. 6 (June 23, 2022): 137–51. http://dx.doi.org/10.33186/1027-3689-2022-6-137-151.

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The author responds to the considerations expressed mainly by E. A. Plesh-kevich in his article “On documentology and its methodology”. The author praises Pleshkevich’s acknowledging the concept of “documentology”. Indeed, the term “document” differs pertaining to the subject (substantially) and object (functional-ly), and this representation makes the foundation for relativeness of this concept in practical applications. Meanwhile, the author points to the disruptiveness of other Pleshkevich’s arguments: alleged unscientific nature of differentiated ap-proach to the concept of “document”, val

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Putra, Fredi Ganda, Dewi Lengkana, Sugeng Sutiarso, et al. "Mathematical representation: A bibliometric mapping of the research literature (2013–2022)." Infinity Journal 13, no. 1 (2023): 1–26. http://dx.doi.org/10.22460/infinity.v13i1.p1-26.

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Mathematical representation ability is an essential skill for students to understand mathematical concepts. Many studies have been conducted regarding this ability, but it is necessary to map existing research to provide a clearer picture of future research topics. This study aims to provide a bibliometric review of trends using mathematical representation skills in mathematics teaching research. The method in this study is bibliometric analysis, which aims to analyze and classify bibliographic material by presenting representative summaries of the literature in the Scopus database. The search

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Duan, Junwen, Xiao Ding, Yue Zhang, and Ting Liu. "TEND: A Target-Dependent Representation Learning Framework for News Document." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 12 (2019): 2313–25. http://dx.doi.org/10.1109/taslp.2019.2947364.

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Liang, Jinglin, Yutao Qin, Shuangping Huang, et al. "Knowledge-embedded graph representation learning for document-level relation extraction." Expert Systems with Applications 295 (January 2026): 128872. https://doi.org/10.1016/j.eswa.2025.128872.

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