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Relevant bibliographies by topics / Database, global network, natural language processing, error

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Academic literature on the topic 'Database, global network, natural language processing, error'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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Journal articles on the topic "Database, global network, natural language processing, error"

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Kolesnikov, Alexey, Egor Plitchenko, and Maria Kropacheva. "Automation of data preparation for mapping using natural language processing systems." InterCarto. InterGIS 28, no. 1 (2022): 659–69. http://dx.doi.org/10.35595/2414-9179-2022-1-28-659-669.

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The current level of development of information technology makes it possible to automate the processing of those types of data that only a specialist could previously work with. One such example is natural language processing technologies that implement the functions of sentiment analysis, machine translation, and question-answer systems. For the processes of creating cartographic and geoinformation works, the methods of extracting named entities are of the greatest interest, which allows extracting geographical names from unstructured text and linking named entities, which make it possible to

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Gupta, B. M., S. M. Dhawan, and Ghouse Modin N. Mamdapur. "Research trends in the field of natural language processing : A scientometric study based on global publications during 2001-2020." COLLNET Journal of Scientometrics and Information Management 17, no. 1 (2023): 61–79. http://dx.doi.org/10.47974/cjsim-2022-0023.

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The study provides a quantitative and qualitative description of global research in “Natural Language Processing” ( NLP) using bibliometric methods. The analysis is based on publications data sourced from Scopus database for the period 2001-2020. The purpose of the study is to understand the status of NLP research at the global, national, institutional, and author level. The study highlights the productivity and performance of NLP research on a series of metrics as well as provides a visual view of collaborative network relationship between authors, research institutions, and leading countries

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Lo, Shaw-Hwa, and Yiqiao Yin. "Language Semantics Interpretation with an Interaction-Based Recurrent Neural Network." Machine Learning and Knowledge Extraction 3, no. 4 (2021): 922–45. http://dx.doi.org/10.3390/make3040046.

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Text classification is a fundamental language task in Natural Language Processing. A variety of sequential models are capable of making good predictions, yet there is a lack of connection between language semantics and prediction results. This paper proposes a novel influence score (I-score), a greedy search algorithm, called Backward Dropping Algorithm (BDA), and a novel feature engineering technique called the “dagger technique”. First, the paper proposes to use the novel influence score (I-score) to detect and search for the important language semantics in text documents that are useful for

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Hang, Ching-Nam, Pei-Duo Yu, Roberto Morabito, and Chee-Wei Tan. "Large Language Models Meet Next-Generation Networking Technologies: A Review." Future Internet 16, no. 10 (2024): 365. http://dx.doi.org/10.3390/fi16100365.

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The evolution of network technologies has significantly transformed global communication, information sharing, and connectivity. Traditional networks, relying on static configurations and manual interventions, face substantial challenges such as complex management, inefficiency, and susceptibility to human error. The rise of artificial intelligence (AI) has begun to address these issues by automating tasks like network configuration, traffic optimization, and security enhancements. Despite their potential, integrating AI models in network engineering encounters practical obstacles including co

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Odisho, Anobel Y., Briton Park, Nicholas Altieri, et al. "Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation." JAMIA Open 3, no. 3 (2020): 431–38. http://dx.doi.org/10.1093/jamiaopen/ooaa029.

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Abstract Objective Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings. Materials and methods Our data comes from the Urologic Outcomes Database at UCSF which includes 3232 annotated prostate cancer pathology reports from 2001 to 2018. We approach 17 separate information extraction tasks, involving a wide range of pathologic features. To handle the diverse range of fields, we required 2 stat

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Fang, Yong, Jian Gao, Zhonglin Liu, and Cheng Huang. "Detecting Cyber Threat Event from Twitter Using IDCNN and BiLSTM." Applied Sciences 10, no. 17 (2020): 5922. http://dx.doi.org/10.3390/app10175922.

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In the context of increasing cyber threats and attacks, monitoring and analyzing network security incidents in a timely and effective way is the key to ensuring network infrastructure security. As one of the world’s most popular social media sites, users post all kinds of messages on Twitter, from daily life to global news and political strategy. It can aggregate a large number of network security-related events promptly and provide a source of information flow about cyber threats. In this paper, for detecting cyber threat events on Twitter, we present a multi-task learning approach based on t

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Muravskyi, Volodymyr. "The impact of global technological trends on accounting." Herald of Ternopil National Economic University, no. 4 (86) (December 12, 2017): 138–48. http://dx.doi.org/10.35774/visnyk2017.04.138.

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The article points out that the pace of technological advance has led to integrating informaion and communication technology into accounting processes. Examples of advanced technologies for business that influence accounting management include computer-assisted learning and artificial intelligence, “smart” applications for telecommunication devices, “smart” things, complemented by virtual reality, digital twins, blockchain, chat communication systems, adaptive security systems, applications and network architecture, integrated electronic platforms. The aim of the research is to elucidate the i

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Tao, Jin, Kelly Brayton, and Shira Broschat. "Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database." Applied Sciences 11, no. 1 (2020): 24. http://dx.doi.org/10.3390/app11010024.

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Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with e

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Wang, Wenshuo. "Enhancing Multimodal Emotion Analysis through Fusion with EMT Model Based on BBFN." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 52–59. http://dx.doi.org/10.62051/gfeqm854.

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Sentiment analysis, as one of the key technologies of natural language processing, has been widely used in medical, film and television fields. In order to increase sentiment analysis's precision, it is particularly important to integrate multi-modal data. This paper presents a pioneering fusion strategy that amalgamates the cutting-edge Efficient Multimodal Transformer (EMT) model with the innovative Bi-Bimodal Fusion Network (BBFN) to revolutionize emotion analysis. By synergistically integrating these two state-of-the-art models, the research endeavors to enhance the efficiency and precisio

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Valcamonico, Dario, Piero Baraldi, Francesco Amigoni, and Enrico Zio. "Natural Language Processing method for the identification of the factors influencing road accident severity." PHM Society European Conference 6, no. 1 (2021): 12. http://dx.doi.org/10.36001/phme.2021.v6i1.2899.

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Although road safety has improved in the last decades, the rate of accidents with severe and fatal consequences is still exceeding the safety objectives (European Commission 2019; World Health Organization 2018).This work explores the possibility of using Natural Language Processing (NLP) techniques for the automatic extraction of knowledge from road accidents reports, with the objective of supporting the safety management of the road infrastructure system (Persia et al. 2016).To this aim, we consider databases of textual reports on road accidents, provided by the local public authorities. The

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Dissertations / Theses on the topic "Database, global network, natural language processing, error"

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Sazonov, Rostislav. "Computer-assisted language learning." Thesis, Молодь у глобалізованому світі: академічні аспекти англомовних фахових досліджень (англ. мовою) / Укл., ред. А.І.Раду: збірник мат. конф. - Львів: ПП "Марусич", 2011. - 147 с, 2011. http://er.nau.edu.ua/handle/NAU/20775.

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Book chapters on the topic "Database, global network, natural language processing, error"

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Schölly, Reto, Suhail Yazijy, and Philipp Kellmeyer. "MedSentinel – A Smart Sentinel for Biomedical Online Search Demonstrated by a COVID-19 Search." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220078.

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We present a work-in-progress software project which aims to assist cross-database medical research and knowledge acquisition from heterogeneous sources. Using a Natural Language Processing (NLP) model based on deep learning algorithms, topical similarities are detected, going beyond measures of connectivity via citation or database suggestion algorithms. A network is generated based on the NLP-similarities between them, and then presented within an explorable 3D environment. Our software will then generate a list of publications and datasets which pertain to a certain topic of interest, based

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Conference papers on the topic "Database, global network, natural language processing, error"

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Kumar, Ashutosh. "Transformer-Based Deep Learning Models for Well Log Processing and Quality Control by Modelling Global Dependence of the Complex Sequences." In Abu Dhabi International Petroleum Exhibition & Conference. SPE, 2021. http://dx.doi.org/10.2118/208109-ms.

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Abstract A single well from any mature field produces approximately 1.7 million Measurement While Drilling (MWD) data points. We either use cross-correlation and covariance measurement, or Long Short-Term Memory (LSTM) based Deep Learning algorithms to diagnose long sequences of extremely noisy data. LSTM's context size of 200 tokens barely accounts for the entire depth. Proposed work develops application of Transformer-based Deep Learning algorithm to diagnose and predict events in complex sequences of well-log data. Sequential models learn geological patterns and petrophysical trends to dete

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