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

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methods for analyzing unknown health risk based on nature language process (nlp)

Methods for analyzing unknown health risk based on nature la...

引用

3rd International conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： Liu, Jianying Jiang, Tao Trade Union Guilin Medical University Guilin China School of Humanities and Management Guilin Medical University Guilin China

ISBN: (纸本)9781643684321

With the rapid spread of epidemic situation, how to quickly analyze health risk factors has become a major challenge in the current public health field. The development of natural language processing (NLP) technology allows us to quickly capture the details of dealing with unknown health risks from massive data. This study uses official case reports on the Chinese Internet since the outbreak of Delta to analyze, and tries to use NLP technology to quickly analyze relevant health risk factors. This research attempts to use NLP-related technologies to interpret the main health risks before the goal, and to provide research methods and ideas for future response to unknown health challenges. This study found that it is difficult for non-public health professionals to understand the health risk factors interpreted by NLP. At present, the use of NLP to analyze cases related to health threats is not practical, and its role is mainly reflected in improving the efficiency of large-scale data analysis and decision support. © 2023 IOS Press. All rights reserved.

关键词： Health risks

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Production Classification in E-Commerce Based on Product Descriptions with natural language processing (NLP) and Machine Learning Models 5th

Production Classification in E-Commerce Based on Product Des...

引用

5th EAI International conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Bist, Yuvraj Gurbaxani, Paramesh Gupta, Neetu Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9783031770746

The rapid growth of the internet has increased people's reliance on it for expressing opinions on products and stores. Text sentiment analysis is now a key research area. Deep learning methods are commonly used for text classification, but they suffer from issues like information loss and weak context. This paper enhances the existing models to simplify the process, educe training time, and improve overall recall and accuracy in text sentiment classification. With the rapid development of artificial intelligence, traditional manual techniques are fading, and AI algorithms are driving the swift progress of text sentiment classification. Product categorization is critical in e-commerce since it influences search, recommendations, inventory, and consumer experience. NLP approaches and models such as TF-IDF and Word2Vec are effective for categorizing products based on their textual descriptions. This literature study investigates the research evolution, problems, and upcoming trends in using TF-IDF and Word2Vec for product classification in e-commerce. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Adversarial machine learning

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ETS: An Error Tolerable System for Coreference Resolution 15

ETS: An Error Tolerable System for Coreference Resolution

引用

15th conference on Computational natural language Learning, CoNLL 2011

作者： Xiong, Hao Song, Linfeng Meng, Fandong Liu, Yang Liu, Qun Lü, Yajuan Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing100190 China

ISBN: (纸本)9781937284084

This paper presents our error tolerable system for coreference resolution in CoNLL-2011(Pradhan et al., 2011) shared task (closed track). Different from most previous reported work, we detect mention candidates based on packed forest instead of single parse tree, and we use beam search algorithm based on the Bell Tree to create entities. Experimental results show that our methods achieve promising results on the development set. © 2011 Association for Computational Linguistics

关键词： Forestry

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Enhancing Question Answering for Enterprise Knowledge Bases using Large language Models 29th

Enhancing Question Answering for Enterprise Knowledge Bases ...

引用

29th International conference on Database Systems for Advanced Applications (DASFAA)

作者： Jiang, Feihu Qin, Chuan Yao, Kaichun Fang, Chuyu Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui Univ Sci & Technol China Hefei Peoples R China BOSS Zhipin Career Sci Lab Beijing Peoples R China Tsinghua Univ PBC Sch Finance Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Hong Kong Univ Sci & Technol GuangZhou Thrust Artificial Intelligence Clear Water Bay Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China Guangzhou HKUST Fok Ying Tung Res Inst Guangzhou Peoples R China

ISBN: (纸本)9789819755615;9789819755622

Efficient knowledge management plays a pivotal role in augmenting both the operational efficiency and the innovative capacity of businesses and organizations. By indexing knowledge through vectorization, a variety of knowledge retrieval methods have emerged, significantly enhancing the efficacy of knowledge management systems. Recently, the rapid advancements in generative natural language processing technologies paved the way for generating precise and coherent answers after retrieving relevant documents tailored to user queries. However, for enterprise knowledge bases, assembling extensive training data from scratch for knowledge retrieval and generation is a formidable challenge due to the privacy and security policies of private data, frequently entailing substantial costs. To address the challenge above, in this paper, we propose EKRG, a novel Retrieval-Generation framework based on large language models (LLMs), expertly designed to enable question-answering for Enterprise Knowledge bases with limited annotation costs. Specifically, for the retrieval process, we first introduce an instruction-tuning method using an LLM to generate sufficient document-question pairs for training a knowledge retriever. This method, through carefully designed instructions, efficiently generates diverse questions for enterprise knowledge bases, encompassing both fact-oriented and solution-oriented knowledge. Additionally, we develop a relevance-aware teacher-student learning strategy to further enhance the efficiency of the training process. For the generation process, we propose a novel chain of thought (CoT) based fine-tuning method to empower the LLM-based generator to adeptly respond to user questions using retrieved documents. Finally, extensive experiments on real-world datasets have demonstrated the effectiveness of our proposed framework.

关键词： Knowledge Management Question Answering Minimal Supervision Large language Model

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Two-stage Deep Learning Method for Sparse-view Computed Tomography Image Reconstruction

Two-stage Deep Learning Method for Sparse-view Computed Tomo...

引用

natural language processing (ICNLP), International conference on

作者： Jiapei Cui Xuanxuan Zhang Yunfei Jia Chenhui Ye School of Communication and Information Engineering Xi’an University of Posts & Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Sparse-view computed tomography (CT) is an effective method for reducing radiation dose. The images reconstructed from insufficient data obtained from sparse-view CT suffer from severe star shaped artifacts. Therefore, reducing radiation dose will reduce imaging quality. Although deep learning methods used for sparse-view CT reconstruction have achieved impressive success, such as convolutional neural network (CNN), the reconstruction results are still too smooth, i.e. losing many details. In this work, we propose a two-stage deep learning method to reduce the artifacts of sparse-view CT images. We conducted several numerical simulation experiments to test the performance of the proposed network. The results indicate that the denoising method can significantly reduce the artifacts caused by sparse sampling and carry more detailed information than CNN.

关键词： Deep learning Computed tomography Noise reduction Stars Imaging Numerical simulation natural language processing Convolutional neural networks Image reconstruction

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Intelligent Mobile-Based Campus Navigational Assistant Using natural language processing and Computer Vision: A Case Study for University of Ghana 9

Intelligent Mobile-Based Campus Navigational Assistant Using...

引用

9th IEEE International conference on Adaptive Science and Technology, ICAST 2024

作者： Sowah, Robert A. Sarkodie-Mensah, Baffour Osei, Gifty University of Ghana Department of Computer Engineering Accra Ghana

ISBN: (纸本)9798350385403

Navigating through the University of Ghana campus, like most tertiary campuses, can be very challenging, especially for a freshman, foreign, or an exchange student. There are numerous routes leading to particular buildings, and depending on the route taken, one may not see the inscriptions placed on these buildings. Some buildings do not have any inscription at all, and some of the signboards to the buildings have defaced due to prolonged exposure to harsh environmental conditions. Smartphones are ubiquitous today, and the current trend of conversations among the youth is dominant through mobile chat. Therefore, this project sought to take advantage of these factors to design a mobile-based system to cater to this need by providing a means of building identification and providing map routes to help students find their way around. Neural Networks was employed in the development of the project. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two mainstream methods of deep learning and as such, were incorporated to develop a mobile-based chatbot system (UGBot) that provides an interactive manner of helping students identify buildings by taking pictures of the building and requesting for directions to their intended destinations. After implementing the classifier modules, accuracy values of 96% and 90% were obtained for the image and text classifiers, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Plagiarism Detection Using natural language processing

Automatic Plagiarism Detection Using Natural Language Proces...

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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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YouTube Transcript Summarization using Abstractive and Extractive approaches 2

YouTube Transcript Summarization using Abstractive and Extra...

引用

2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Aneesha Krishna Maiya, U. Akash Reddy, V. Geetha, S. Naidila Sadashiv, C. Department of Computer Science and Engineering JSS Academy of Technical Education Bengaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

YouTube is one of major video-sharing platform in today's world. Every minute hundreds of hours of video content are being added to it. Extracting key information from such massive amount of video content is very much required. This can be done using summarizing the video's transcript highlighting important information. In this paper we have used three most commonly used extractive summarization techniques namely Luhn's algorithm, TextRank algorithm, Keyword-based summarization and a custom algorithm combining the methods used in these three techniques. Additionally, we have used popular abstractive pre-trained models such as BART, T5 and Pegasus. Both of the abstractive and extractive methods are compared based on ROUGE scores on CNN/DailyMail dataset. We will be looking at merits and demerits of both of these approaches. Based on the ROUGE scores and time taken to generate summary we can conclude that Luhn Algorithm is the best method among extractive summarization techniques and PEGASUS is the best pretrained model suitable for this task. © 2024 IEEE.

关键词： natural language processing systems

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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...

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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

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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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