Relevant bibliographies by topics / Representation Learning; Author and Document

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Representation Learning; Author and Document'

Author: Grafiati
Published: 22 June 2024
Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "Representation Learning; Author and Document"

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Terreau, Enzo. "Apprentissage de représentations d'auteurs et d'autrices à partir de modèles de langue pour l'analyse des dynamiques d'écriture." Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20001.

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La démocratisation récente et massive des outils numériques a donné à tous le moyen de produire de l'information et de la partager sur le web, que ce soit à travers des blogs, des réseaux sociaux, des plateformes de partage, ... La croissance exponentielle de cette masse d'information disponible, en grande partie textuelle, nécessite le développement de modèles de traitement automatique du langage naturel (TAL), afin de la représenter mathématiquement pour ensuite la classer, la trier ou la recommander. C'est l'apprentissage de représentation. Il vise à construire un espace de faible dimension
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Sayadi, Karim. "Classification du texte numérique et numérisé. Approche fondée sur les algorithmes d'apprentissage automatique." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066079/document.

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Différentes disciplines des sciences humaines telles la philologie ou la paléographie font face à des tâches complexes et fastidieuses pour l'examen des sources de données. La proposition d'approches computationnelles en humanités permet d'adresser les problématiques rencontrées telles que la lecture, l'analyse et l'archivage de façon systématique. Les modèles conceptuels élaborés reposent sur des algorithmes et ces derniers donnent lieu à des implémentations informatiques qui automatisent ces tâches fastidieuses. La première partie de la thèse vise, d'une part, à établir la structuration thém
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Wauquier, Pauline. "Task driven representation learning." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30005/document.

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De nombreux algorithmes d'Apprentissage automatique ont été proposés afin de résoudre les différentes tâches pouvant être extraites des problèmes de prédiction issus d'un contexte réel. Pour résoudre les différentes tâches pouvant être extraites, la plupart des algorithmes d'Apprentissage automatique se basent d'une manière ou d'une autre sur des relations liant les instances. Les relations entre paires d'instances peuvent être définies en calculant une distance entre les représentations vectorielles des instances. En se basant sur la représentation vectorielle des données, aucune des distance
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Dos, Santos Ludovic. "Representation learning for relational data." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066480/document.

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L'utilisation croissante des réseaux sociaux et de capteurs génère une grande quantité de données qui peuvent être représentées sous forme de graphiques complexes. Il y a de nombreuses tâches allant de l'analyse de l'information à la prédiction et à la récupération que l'on peut imaginer sur ces données où la relation entre les noeuds de graphes devrait être informative. Dans cette thèse, nous avons proposé différents modèles pour trois tâches différentes: - Classification des noeuds graphiques - Prévisions de séries temporelles relationnelles - Filtrage collaboratif. Tous les modèles proposés
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Belharbi, Soufiane. "Neural networks regularization through representation learning." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR10/document.

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Les modèles de réseaux de neurones et en particulier les modèles profonds sont aujourd'hui l'un des modèles à l'état de l'art en apprentissage automatique et ses applications. Les réseaux de neurones profonds récents possèdent de nombreuses couches cachées ce qui augmente significativement le nombre total de paramètres. L'apprentissage de ce genre de modèles nécessite donc un grand nombre d'exemples étiquetés, qui ne sont pas toujours disponibles en pratique. Le sur-apprentissage est un des problèmes fondamentaux des réseaux de neurones, qui se produit lorsque le modèle apprend par coeur les d
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Vukotic, Verdran. "Deep Neural Architectures for Automatic Representation Learning from Multimedia Multimodal Data." Thesis, Rennes, INSA, 2017. http://www.theses.fr/2017ISAR0015/document.

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La thèse porte sur le développement d'architectures neuronales profondes permettant d'analyser des contenus textuels ou visuels, ou la combinaison des deux. De manière générale, le travail tire parti de la capacité des réseaux de neurones à apprendre des représentations abstraites. Les principales contributions de la thèse sont les suivantes: 1) Réseaux récurrents pour la compréhension de la parole: différentes architectures de réseaux sont comparées pour cette tâche sur leurs facultés à modéliser les observations ainsi que les dépendances sur les étiquettes à prédire. 2) Prédiction d’image et
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Karpate, Yogesh. "Enhanced representation & learning of magnetic resonance signatures in multiple sclerosis." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S068/document.

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La sclérose en plaques (SEP) est une maladie auto-immune inflammatoire du jeune adulte causant des handicaps variables et progressifs irréversibles. Cette maladie est présente de manière prépondérante dans l’hémisphère nord. Cette thèse s’attache à la caractérisation et à la modélisation de signatures IRM multimodales des lésions de sclérose en plaques. L’objectif est d’améliorer les modèles de représentation de l’image et d’adapter les méthodes d’apprentissage pour la reconnaissance visuelle, dans le cas où des informations de haut niveau telles que les lésions SEP incluses dans l’IRM sont ex
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Soltan-Zadeh, Yasaman. "Improved rule-based document representation and classification using genetic programming." Thesis, Royal Holloway, University of London, 2011. http://repository.royalholloway.ac.uk/items/479a1773-779b-8b24-b334-7ed485311abe/8/.

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Lu, Ying. "Transfer Learning for Image Classification." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEC045/document.

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Lors de l’apprentissage d’un modèle de classification pour un nouveau domaine cible avec seulement une petite quantité d’échantillons de formation, l’application des algorithmes d’apprentissage automatiques conduit généralement à des classifieurs surdimensionnés avec de mauvaises compétences de généralisation. D’autre part, recueillir un nombre suffisant d’échantillons de formation étiquetés manuellement peut s’avérer très coûteux. Les méthodes de transfert d’apprentissage visent à résoudre ce type de problèmes en transférant des connaissances provenant d’un domaine source associé qui contient
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Alaverdyan, Zaruhi. "Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI005/document.

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Cette étude vise à développer un système d’aide au diagnostic (CAD) pour la détection de lésions épileptogènes, reposant sur l’analyse de données de neuroimagerie, notamment, l’IRM T1 et FLAIR. L’approche adoptée, introduite précédemment par Azami et al., 2016, consiste à placer la tâche de détection dans le cadre de la détection de changement à l'échelle du voxel, basée sur l’apprentissage d’un modèle one-class SVM pour chaque voxel dans le cerveau. L'objectif principal de ce travail est de développer des mécanismes d’apprentissage de représentations, qui capturent les informations les plus d
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Books on the topic "Representation Learning; Author and Document"

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Edwards, Carolyn, Lella Gandini, and George Forman, eds. The Hundred Languages of Children. 3rd ed. ABC-CLIO, LLC, 2011. http://dx.doi.org/10.5040/9798400667664.

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Why does the city of Reggio Emilia in northern Italy feature one of the best public systems of early education in the world? This book documents the comprehensive and innovative approach that utilizes the "hundred languages of children" to support their well-being and foster their intellectual development. Educators in Reggio Emilia, Italy, use a distinctive innovative approach that supports children's well-being and fosters their intellectual development through a systematic focus on symbolic representation. From birth through age six, young children are encouraged to explore their environmen
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Book chapters on the topic "Representation Learning; Author and Document"

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Liu, Zhiyuan, Yankai Lin, and Maosong Sun. "Document Representation." In Representation Learning for Natural Language Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5573-2_5.

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Kamkarhaghighi, Mehran, Eren Gultepe, and Masoud Makrehchi. "Deep Learning for Document Representation." In Handbook of Deep Learning Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11479-4_5.

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Ding, Ning, Yankai Lin, Zhiyuan Liu, and Maosong Sun. "Sentence and Document Representation Learning." In Representation Learning for Natural Language Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_4.

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AbstractSentence and document are high-level linguistic units of natural languages. Representation learning of sentences and documents remains a core and challenging task because many important applications of natural language processing (NLP) lie in understanding sentences and documents. This chapter first introduces symbolic methods to sentence and document representation learning. Then we extensively introduce neural network-based methods for the far-reaching language modeling task, including feed-forward neural networks, convolutional neural networks, recurrent neural networks, and Transfo
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Cosma, Adrian, Mihai Ghidoveanu, Michael Panaitescu-Liess, and Marius Popescu. "Self-supervised Representation Learning on Document Images." In Document Analysis Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57058-3_8.

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Wu, Xiaoyun, Rohini Srihari, and Zhaohui Zheng. "Document Representation for One-Class SVM." In Machine Learning: ECML 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30115-8_45.

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Saeidi, Mozhgan, Evangelos Milios, and Norbert Zeh. "Graph Representation Learning in Document Wikification." In Document Analysis and Recognition – ICDAR 2021 Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86159-9_37.

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López-Monroy, Adrián Pastor, Manuel Montes-y-Gómez, Luis Villaseñor-Pineda, Jesús Ariel Carrasco-Ochoa, and José Fco Martínez-Trinidad. "A New Document Author Representation for Authorship Attribution." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31149-9_29.

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Zhang, Yue, Liying Zhang, and Yao Liu. "Linked Document Classification by Network Representation Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01716-3_25.

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Feng, Hao, Wengang Zhou, Jiajun Deng, Yuechen Wang, and Houqiang Li. "Geometric Representation Learning for Document Image Rectification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19836-6_27.

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Li, Luyang, Wenjing Ren, Bing Qin, and Ting Liu. "Learning Document Representation for Deceptive Opinion Spam Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25816-4_32.

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Conference papers on the topic "Representation Learning; Author and Document"

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Zhu, Wenhao, Ziyue Yu, Kakit Chio, and Wuman Luo. "Label Distribution Representation Learning in Document-Level Sentiment Analysis." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719196.

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Li, Ruochen, Zimu Wang, and Xinya Du. "Efficient Document-level Event Relation Extraction." In Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.repl4nlp-1.7.

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Dai, Qizhu, Jiang Zhong, Kuan Li, et al. "Contrastive Learning Enhanced Graph Relation Representation for Document-level Relation Extraction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822629.

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Taechoyotin, Pawin, and Daniel Acuna. "MISTI: Metadata-Informed Scientific Text and Image Representation through Contrastive Learning." In Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.sdp-1.15.

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Zhuang, Jun, and Casey Kennington. "Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning." In Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.sdp-1.6.

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Rodrigues, Natan S., and Celia G. Ralha. "A Hybrid Machine Learning Method to Author Name Disambiguation." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/stil.2024.245440.

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Digital bibliographic repositories, including publications, authors, and research fields are essential for sharing scientific information. Nevertheless, the information retrieval, extraction, and classification efficiency in such archives is threatened by author name ambiguity. This paper addresses the Author Name Disambiguation (AND) problem by proposing a hybrid machine learning method integrating Bidirectional Encoder Representations from Transformers (BERT), Graph Convolutional Network (GCN), and Graph Enhanced Hierarchical Agglomerative Clustering (GHAC) approaches. The BERT model extract
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Li, Peizhao, Jiuxiang Gu, Jason Kuen, et al. "SelfDoc: Self-Supervised Document Representation Learning." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00560.

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Frery, Jordan, Christine Largeron, and Mihaela Juganaru-Mathieu. "Author identification by automatic learning." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333748.

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Chen, Xiuying, Shen Gao, Chongyang Tao, Yan Song, Dongyan Zhao, and Rui Yan. "Iterative Document Representation Learning Towards Summarization with Polishing." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-1442.

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Menon, Remya, K. Harikrishnan, and Ganesh Varier. "Parallel Approach for Document Representation using Dictionary Learning." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993114.

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Reports on the topic "Representation Learning; Author and Document"

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Church, Joshua, LaKenya Walker, and Amy Bednar. Iterative Learning Algorithm for Records Analysis (ILARA) user manual. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41845.

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This manual is intended for new users with minimal or no experience with using the Iterative Learning Algorithm for Records Analysis (ILARA) tool. The goal of this document is to give an overview of the main functions of ILARA. The primary focus of this document is to demonstrate functionality. Every effort has been made to ensure this document is an accurate representation of the functionality of the ILARA tool. For additional information about this manual, contact ERDC.JAIC@erdc.dren.mil.
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Learning About Women and Urban Services in Latin America and the Caribbean. Population Council, 1986. http://dx.doi.org/10.31899/pgy1986.1000.

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In 1978 when the Population Council formulated a program to learn more about low-income urban women’s access to services, the dearth of information was striking, particularly in contrast to the emerging body of information delineating access to credit, extension, membership in rural institutions, and representation in local governments. Access to services was much less well-defined owing to the diverse cultures that meet in the urban environment, the mobility of city life, and the fluidity of households. Urban development planners, researchers, and those involved in community action projects i
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