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Classification of the Top-cited Literature by Fusing Linguistic and Citation information with the Transformer Model 18

Classification of the Top-cited Literature by Fusing Linguis...

引用

18th international conference on web information Systems and Technologies, webIST 2022

作者： Ochi, Masanao Shiro, Masanori Mori, Jun’ichiro Sakata, Ichiro Department of Technology Management for Innovation Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo Japan HIRI National Institute of Advanced Industrial Science and Technology Umezono 1-1-1 Ibaraki Tsukuba Japan

ISBN: (纸本)9789897586132

the scientific literature contains a wide variety of data, including language, citations, and images of figures and tables. the Transformer model, released in 2017, was initially used in natural language processing but has since been widely used in various fields, including image processing and network science. Many Transformer models trained with an extensive data set are available, and we can apply small new data to the models for our focused tasks. However, classification and regression studies for scholarly data have been conducted primarily by using each data set individually and combining the extracted features, with insufficient consideration given to the interactions among the data. In this paper, we propose an end2end fusion method for linguistic and citation information in scholarly literature data using the Transformer model. the proposed method shows the potential to efficiently improve the accuracy of various classifications and predictions by end2end fusion of various data in the scholarly literature. Using a dataset from the web of Science, we classified papers with the top 20% citation counts three years after publication. the results show that the proposed method improves the F-value by 2.65 to 6.08 percentage points compared to using only particular information. Copyright © 2022 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved.

关键词： Classification (of information)

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Arabic Historical Documents Layout Analysis using Mask RCNN 6

Arabic Historical Documents Layout Analysis using Mask RCNN

引用

6th international conference on AI in Computational Linguistics, ACLing 2024

作者： Aljiffry, Latifa Al-Barhamtoshy, Hassanin Abukhodair, Felwa Jamal, Amani Faculty of Computer and Information Technology King Abdulaziz University Jeddah Saudi Arabia

In recent times, there has been a notable surge in the interest of researchers in the realm of document analysis and optical character recognition (OCR). Significant advancements have been made in OCR engines across various languages, encompassing both printed and handwritten documents. However, there has been a comparatively lower focus on processing documents written in Arabic when juxtaposed with languages like English. this discrepancy arises from several factors, including the inherent challenges posed by the Arabic language and the limited availability of Arabic document datasets. To implement any OCR engine, the initial step involves analyzing the layout of images before subjecting them to the OCR process. this thesis specifically delves into the realm of layout analysis for historical Arabic documents, employing a deep learning (DL) approach. the chosen methodology utilizes the Mask Region-based Convolutional Neural Network (RCNN). the dataset employed consists of historical Arabic documents, particularly early printed ones, each characterized by unique sizes, structures, and processing prerequisites. processing historical documents is inherently more challenging due to factors such as the document's layout structure, distinctive handwriting styles of the authors, paper aging, historical timeframe, ink properties, and more. the achieved accuracy result is 51.14%. When juxtaposed with other existing models, it becomes evident that this work attains a state-of-the-art status, showcasing an impressive outcome. © 2024 the Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Key Phrase Extraction based on Pre-trained language Models 6

Key Phrase Extraction based on Pre-trained Language Models

引用

6th international conference on Electronic information technology and Computer Engineering, EITCE 2022

作者： Yao, Shunyu Hu, Jie Sun, Chuxiong Gao, Zhiqiao Liu, Ning Big Data and Artificial Intelligence Institute China Telecom Research Institute Beijing China

ISBN: (纸本)9781450397148

With the explosion of information and a large amount of data appearing every moment, it is a meaningful task to quickly find the information people want to know in a large amount of text and to present long texts in a streamlined form. Key phrase extraction, which aims to extract from documents a collection of key phrases that express the topic and content of the document, is important for text processing tasks such as information retrieval and document classification and can provide readers with a more comprehensive overview of the topic. We use two types of pre-trained language models for key phrase extraction, namely DeBERTa and RoBERTa, which are first pre-trained on the dataset and then fine-tuned, and the experimental results of these models proved that DeBERTa-V3-Large has reached an F1 score of 0.8925, which is the best result among these models. © 2022 Association for Computing Machinery.

关键词： Text processing

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Design Application of Poetry Communication Platform Based on HanLP

Design Application of Poetry Communication Platform Based on...

引用

international conference on Communications, information System and Computer Engineering (CISCE)

作者： Liang Zhao Guoqiang Li Hongju Liu Information Engineering University Zhengzhou China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

With the popularity of intelligent devices such as computers and mobile Internet, many traditional cultures are developing towards the integration of digital information and intelligence. Establishing a cultural digital platform combined with information technology is of great significance for the development of digital culture industry. this paper is based on HanLP natural language processing technology, combined with poetry and software system characteristics. the poetry communication platform based on HanLP is designed, which has the characteristic modules of poetry and related information retrieval, management and intelligent recommendation.

关键词： Computers Industries Java Software systems information retrieval Natural language processing Internet

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Conversion of Indian Sign language to Speech by Using Deep Neural Network 6

Conversion of Indian Sign Language to Speech by Using Deep N...

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6th international conference on Computing, Communication, Control and Automation, ICCUBEA 2022

作者： Patil, Sonali Gulave, Shivam Gawai, Vaibhav Gode, Prashant Mudme, Prathamesh Pimpri Chinchwad College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9781665484510

Sign language is the most used non-verbal communication method. It is mostly used by persons who have hearing and speaking disabilities. In this world, normal people find it difficult to interpret sign language, so it is very difficult to communicate with mute and deaf people. Mute and deaf people aren't able to express their thoughts to the world. To interpret the meaning of sign language an interpreter is needed. To eliminate the need of an interpreter a system is proposed using neural network. the proposed system can easily interpret the meaning of human hand sign to convert it into relevant speech. Our system has been implemented by using a deep learning technique, Convolution Neural Network (CNN). the hand images are pre-processed using canny edge detection technique for better accuracy. After pre-processing, the classification and prediction steps are applied. the speech synthesizer is used to convert text into speech using Google text to speech API. the proposed system achieves accuracy of up to 98%. © 2022 IEEE.

关键词： Audition

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Signature Based Authentication: A Multi-label Classification Approach to Detect the language and Forged Sample in Signature 1

引用

6th international conference on Computer Vision and Image processing, CVIP 2021

作者： Jain, Anamika Singh, Satish Kumar Singh, Krishna Pratap Centre for Advanced Studies AKTU Lucknow India Indian Institute of Information Technology Allahabad India

ISBN: (数字)9783031113468

ISBN: (纸本)9783031113451

In this work, we have proposed a multi-label classification algorithm for signature images that can be used to solve multiple objectives: i) It can tell the identity of the image. ii) Can interpret the language of the content written in the image. iii) It can also identify whether the given image is the genuine signature of the person or the forged one. this paper has used the pretrained model GoogLeNet, that has been finetuned on the largest signature dataset present (GPDS). GoogLeNet is used to extract the features from the signature images, and these features are fed to the three-layer neural network. the neural network has been used for the classification of the features of the image. the model has been trained or tested against two regional datasets Hindi and Bengali datasets. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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Multi-Lingual Sentence Alignment with GPT Models 4

Multi-Lingual Sentence Alignment with GPT Models

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Liang, Xiao Khaw, Yen-Min Jasmina Liew, Soung-Yue Tan, Tien-Ping Qin, DongHong Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Department of Computer Science Kampar Malaysia School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Artificial Intelligence GuangXi MinZu University NanNing China

ISBN: (纸本)9798350318432

this paper investigates sentence alignment technology, an essential element in Natural language processing tasks like machine translation, that pairs corresponding sentences within bilingual documents. Despite the existence of standard bilingual alignment corpora, they often fall short in covering low-resource languages and specific domains, underscoring the need for advanced sentence alignment algorithms. We explore the potential of the GPT model for sentence alignment, focusing on aspects like prompt construction, experiment design, and results evaluation. Utilizing the OPUS corpus, our experimental design covers eight commonly used languages in 28 directions. the study reveals that the GPT-aligner outperforms traditional algorithms by 5-30% in most scenarios, with the performance contingent on language, prompting, the GPT system model, and few-shot demonstration strategies. this work illuminates the significant potential of sentence alignment algorithms in the future development of language models. © 2023 IEEE.

关键词： Design of experiments

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Computer and web technology in Teaching Management System of Colleges and Universities 6

Computer and Web Technology in Teaching Management System of...

引用

6th international conference on Digital technology in Education, ICDTE 2022

作者： Zhang, Pingping Chongqing College of Architecture and Technology Chongqing China

ISBN: (纸本)9781450398091

Recently, the scale of running colleges and universities has continued to expand, the professional environment is very flexible, the teaching methods are diverse, and the school-enterprise cooperation is frequent, which undoubtedly increases the workload of teaching management. thus, it is imperative to fully use modern information technology, establish a teaching management system, and improve the level of teaching management and school operation efficiency. the purpose of this paper is to study the college teaching management system based on computer and Internet technology. It expounds the general situation of teaching management in colleges and universities, formulates the design scheme of teaching management system in colleges and universities, and designs in detail around the modules of teaching management, examination management, system management, and student management. Using *** technology to create and manage web application platform, using the combination of B/S and C/S as the development language, developed and realized 7 functional modules of SQL Server database system. Finally, the performance test and system operation test are gotten. the results show that the average response time of each information input test is 2.5 seconds, and the CPU utilization rate is 27%, indicating that the information input is correct and the query and save results are correct. © 2022 Association for Computing Machinery.

关键词： Students

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NLP-Driven ML for Resume information Extraction

NLP-Driven ML for Resume Information Extraction

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international conference for Convergence of technology (I2CT)

作者： Kedar Gawhankar Ashutosh Deorukhkar Aditi Miniyar Hitesh Kapure Bincy Ivin Dept. of Information Technology Vivekanand Education Society’s Institute Of Technology Chembur Maharashtra India Dept. of Electronics and Telecommunication Vivekanand Education Society’s Institute Of Technology Chembur Maharashtra India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In the competitive realm of job seeking, resumes act as a ticket to various opportunities that unlock a professional venture. this research paper unveils a transformed web application tailored for streamlined resume parsing. the project combines the technology of Natural language processing (NLP), user-friendly design principles, and robust web infrastructure to create an innovative tool for automating the resume analysis process. Adapting the advanced NLP techniques, the application efficiently extracts essential information from diverse resume formats, including skills, education, work experience, and contact details. By adopting the power of NLP libraries and methodologies, the system optimizes the accuracy and efficiency of the parsing process. By providing a thorough tool that enhances the efficiency of candidate evaluation, the project addresses a critical need in modern recruitment processes.

关键词： Resumes Education Companies Forestry information retrieval Natural language processing Libraries

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CWITR: A Corpus for Automatic Complex Word Identification in Turkish Texts 6

CWITR: A Corpus for Automatic Complex Word Identification in...

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6th international conference on Natural language processing and information Retrieval, NLPIR 2022

作者： Ilgen, Bahar Biemann, Chris Language Technology Group Department of Informatics University of Hamburg Germany

ISBN: (纸本)9781450397629

the Complex Word Identification (CWI) task aims to provide support to resolve accessibility barriers for people who experience difficulties with cognitive, language, and learning disabilities. the task is concerned with the detection and identification of complex words that are unusual and difficult to understand by certain target groups. CWI systems have a large impact on the output of Text Simplification (TS) systems. this paper revisits the CWI task by extending available datasets by creating a new CWI corpus. In this study, we collect a new CWI dataset (CWITR) of complex single and multi-token words consisting of different text genres for Turkish and prepare it for investigation of computational methods on discrimination between complex and non-complex words forms. © 2022 ACM.

关键词： Crowdsourcing

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