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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Automatic Plagiarism Detection Using natural language processing

Automatic Plagiarism Detection Using Natural Language Proces...

引用

International conference on Computing for Sustainable Global Development (INDIACom)

作者： Surya Kant Pal Omar Junior Raffik Rita Roy Vishwakarma Bipin Lalman Shristee Srivastava Bhoomi Sharma Department of Mathematics SSBSR Sharda University Greater Noida U.P India Department of Computer Science and Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam A.P. India

Plagiarism detectors are software or apps that are primarily intended for string-level comparisons between texts that raise the suspicion of being plagiarized and texts that have the potential to be original work. By incorporating natural language processing (NLP) methods into the currently in use detection methods, this study seeks to increase the accuracy of plagiarism detectors. A framework for plagiarism detectors was proposed, in which sets of natural language processing (NLP) techniques were applied to several available documents deemed suspicious and authentic. The work was not limited to analyzing the given documents, but also involved understanding the structure of the text and accounting for text relations. Using the methods on a corpus of short paragraphs revealed the level of improvement that NLP techniques had, as well as their increased accuracy in identifying plagiarism.

关键词： Plagiarism Computational modeling Detectors Feature extraction natural language processing Software Faces

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Breaking Text Limits: Exploring Frontier Technologies and Challenges in Parsing Massive Log Files with GPT

Breaking Text Limits: Exploring Frontier Technologies and Ch...

引用

Digital Society and Intelligent Systems (DSInS), International conference on

作者： Zhijing Li Yiping Jiang Hong Li Ying Wang Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Large-scale log files are vital for capturing critical information about system operations, network interactions, and user activities, making them essential in areas such as enterprise management, research, and cybersecurity. However, as data volume grows exponentially, traditional text processing methods struggle to efficiently parse these extensive logs, hindering accurate information extraction and analysis. This study focuses on leveraging existing natural language processing techniques, including regular expressions, keyword extraction, and text clustering, to tackle the challenges of parsing large log files. We examine the specific issues posed by such data and introduce a comprehensive approach that incorporates log preprocessing with regular expressions, followed by key information extraction via advanced algorithms, and segmentation through text clustering methods. Our experimental results demonstrate an accuracy rate of 85%, highlighting the effectiveness and potential of our proposed solution for addressing the increasing complexity of log file analysis.

关键词： Accuracy Systems operation Clustering methods Clustering algorithms Information retrieval natural language processing Complexity theory Intelligent systems Computer security Text processing

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Neural language Models in natural language processing

Neural Language Models in Natural Language Processing

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International conference on

作者： Zihao Chen Taiyuan University of Technology Taiyuan China

People cannot live without natural language in their daily life, and natural language is also an essential part of the heritage of human civilization. With the rapid development of information technology and the explosive growth of various data, natural language processing (NLP) technology based on deep learning and other technologies in the field of artificial intelligence has emerged as the times require. With the rapid development of deep learning models in recent years, breakthroughs have been made in the field of natural language processing. Based on recent research, this paper briefly introduces the development process of deep learning, the concepts of deep learning and NLP, the methods of deep learning used to solve the core problems of NLP, the application of the neural network model in natural language modeling. This paper summarizes the current development achievements and forecasts its future development.

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Automated Citation Matching and Relevancy Score Analysis for Scientific Papers

Automated Citation Matching and Relevancy Score Analysis for...

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2024 conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Myat, Aung Myint Htun, Kyaw Myo Kyaw, Htin Thein, Kyaw Min Htoo, Thet Paing Myo, Aung Kyaw Defence Services Academy Department of Computer Science Pyin Oo Lwin Myanmar Zelenograd Moscow Russia

ISBN: (纸本)9798350360646

Acknowledging the scientific contribution of previous research work is necessary to ensure a smooth evolution in scientific fields. Citations play an important role in the ranking of authors, journals, institutions, and organizations. Research papers need to contain appropriate citations to previous works. The identification of inline citations and matching these citations to their corresponding bibliographic references remained an open area of research. The inline citation extraction and matching are performed using citation anchor taxonomy and rule-based method. To determine the relevance of citing and cited articles, three similarity concepts are introduced: metadata-based, citation content, and content-based, calculated using the Bag-of-Words model, cosine similarity, and text similarity methods. The in-text citation identification outperformed the F Score of 97% and the proposed method achieved an NDCG score of 0.78. The results show that the automated citation matching and providing the relevancy of citing and cited papers based on both contents and references. © 2024 IEEE.

关键词： Bag of Words Cosine Similarity In-text Citations Metadata natural language processing Research Papers Similarity Measures

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Analysis of 50 Years of Health Food Research Trends Using natural language processing and Generative AI 28th

Analysis of 50 Years of Health Food Research Trends Using Na...

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28th International conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Kobayashi, Yoshiyuki Uchida, Takumi Inoue, Takahiro Iwasaki, Yusuke Ito, Rie Saito, Koichi Akiyama, Hiroshi Tsuda, Kazuhiko Yoshida, Kenichi School of Pharmacy and Pharmaceutical Sciences Hoshi University 2-4-41 Ebara Shinagawa-Ku Tokyo142-8501 Japan University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba-shi305-8577 Japan SCI-Analytics Kuwano Bldg. 2F 6-23-4 Jingumae Shibuya-ku Tokyo150-0001 Japan

This study investigates trends in health food research from 1975 to 2024 using text mining and generative AI. Analyzing 92,028 journal entries from Scopus revealed a significant increase in publications, with key topics including nutrients, bioactive compounds, disease prevention, and research methods. AI-assisted classification yielded 12 categories reflecting the field's multidisciplinary nature. Trends include a growing focus on nutrients, health conditions, study design, and food production and consumption context. The findings highlight the increasingly interdisciplinary landscape of health food research, informing future directions and evidence-based decision-making. © 2024 The Authors.

关键词： Food ingredients

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Data Augmentation for Dementia Detection in Spoken language 23

Data Augmentation for Dementia Detection in Spoken Language

引用

Interspeech conference

作者： Hledikova, Anna Woszczyk, Dominika Acman, Alican Demetriou, Soteris Schuller, Bjoern Imperial Coll London Dept Comp London England Univ Augsburg Chair Embedded Intelligence Hlth Care & Wellbeing Augsburg Germany Amazon Bellevue WA USA

Dementia is a growing problem as our society ages, and detection methods are often invasive and expensive. Recent deep-learning techniques can offer a faster diagnosis and have shown promising results. However, they require large amounts of labelled data which is not easily available for the task of dementia detection. One effective solution to sparse data problems is data augmentation, though the exact methods need to be selected carefully. To date, there has been no empirical study of data augmentation on Alzheimer's disease (AD) datasets for NLP and speech processing. In this work, we investigate data augmentation techniques for the task of AD detection and perform an empirical evaluation of the different approaches on two kinds of models for both the text and audio domains. We use a transformer-based model for both domains, and SVM and Random Forest models for the text and audio domains, respectively. We generate additional samples using traditional as well as deep learning based methods and show that data augmentation improves performance for both the text- and audio-based models and that such results are comparable to state-of-the-art results on the popular ADReSS set, with carefully crafted architectures and features(1).

关键词： dementia detection data augmentation speech

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Fusion of Extractive and Abstractive Text Summarization Techniques for Legal documents - An Experimental Evaluation 15

Fusion of Extractive and Abstractive Text Summarization Tech...

引用

15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Avaniya, A. Sathya, S. Siva Sophie, S. Lourdumarie Department of Computer Science Pondicherry University Pondicherry India

ISBN: (纸本)9798331300579

Summarizing legal case documents is a very daunting task as law practitioners have to traverse via a hundreds of case reports before determining the relevant case which may aid as a good resource material in an ongoing case. Numerous summarization algorithms have been proposed over time, including generic text summarization and a few specifically for summarizing legal documents. This paper proposes different hybrid text summarization techniques by the fusion of extractive and abstractive text summarizer for legal domain. The fusion techniques are aimed at generating high-quality abstractive summaries that balance original content preservation with enhanced readability and coherence. The extractive methods namely LSTM-based summarization, CaseSummarizer, and Maximum Marginal Relevance (MMR), are explored in conjunction with Legal Pegasus, a state-of-the-art abstractive summarization model tailored for legal texts. Evaluation using ROUGE metrics demonstrates the effectiveness of these hybrid approaches in generating coherent and contextually relevant summaries within the legal domain. The CaseSummarizer and Legal Pegasus combination emerges as the most promising, achieving the highest F-score across all evaluated metrics, highlighting its potential for improving legal text comprehension and summarization. The proposed approach aims to elevate performance and deliver summaries that are not only contextually relevant but also more comprehensive. © Grenze Scientific Society, 2024.

关键词： natural language processing systems

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Scientific Reading Comprehension with Sentences Selection and Ranking 12th

Scientific Reading Comprehension with Sentences Selection a...

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12th National CCF conference on natural language processing and Chinese Computing, NLPCC 2023

作者： Chen, Jialei Wang, Weihua Shao, Shuai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9783031446986

Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. However, in the scientific domain, the original text is longer than the general domain. In this paper, we proposed a model that consists of a content retrieval module and a pre-trained model module. The content retrieval module finds the most semantically relevant sentences from the text and re-rank them. The seleted sentences and question will be input into the pre-trained model to get the answers. This model could overcome the length limitation of Transformer model length while achieving impressive results. Our model achieved 0.45 score of RougeL, resulting in the second place in the NLPCC2023 Shared Task2. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

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Empowering a Nutritional Lifestyle through Soaked and Streamed Nuts Water Reuse using Novel RNN and NLP

Empowering a Nutritional Lifestyle through Soaked and Stream...

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IT Innovation and Knowledge Discovery (ITIKD), International conference on

作者： Lekhya K Surendran R Logapriya E Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

This study evaluates the effectiveness of a Novel Recurrent Neural Network (RNN) against natural language processing (NLP) methods for nutrient extraction from soaked and steamed nut water. Research Instruments and Methodologies: Ten nutrients were retrieved utilizing advanced RNN and NLP approaches. The iteration value was estimated using G-power analysis with a power of 0.8 and a 95% confidence range. The findings indicated that the Novel RNN method attained an accuracy of 84.10%, surpassing the NLP approach's 71.48%. A statistical analysis indicated that the two approaches exhibited a substantial difference, evidenced by a two-tailed p-value of 0.000 (p < 0.05). The results indicate that the Novel RNN surpasses NLP methods in nutrient extraction accuracy, rendering it a more dependable option for this work.

关键词： Technological innovation Recurrent neural networks Accuracy Statistical analysis Instruments Nutrients Prediction algorithms Rendering (computer graphics) Knowledge discovery natural language processing

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natural language processing for Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience

Natural Language Processing for Identifying Patients With In...

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2023 International conference on Industry Sciences and Computer Science Innovation, iSCSi 2023

作者： Stemmer, Maya Ravid, Gilad Parmet, Yisrael Ben-Gurion University of the Negev P.O.B 653 Beer Sheva84105 Israel

This study provides a framework for identifying patients with Inflammatory Bowel Disease (IBD) on Twitter and learning from their personal experiences. First, we built a user classifier that distinguishes IBD patients from other Twitter users. We constructed classification features from the user's behavior on Twitter, the content of their tweets, and their social network. We compared the performances of five algorithms within two classification approaches. One classified each tweet and deduced the user's class from their tweet-level classification. The other aggregated tweet-level features to user-level features and then classified the users themselves. Both approaches showed promising classification results. Then, we used the classifier to analyze patients' tweets related to health and nutrition. We identified frequently mentioned lifestyles and the patients' sentiments toward them. The findings correlated with what is known about suitable nutrition for IBD. The methods can be adapted to other diseases and enhance medical research regarding chronic conditions. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Nutrition

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