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ODE-CF: An Ordinary Differential Equation Cascaded Fusion Model for Chinese Named Entity Recognition

ODE-CF: An Ordinary Differential Equation Cascaded Fusion Mo...

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Natural language processing (ICNLP), international conference on

作者： Haiyan Liu Zhou Jin Rujing Wang Jie Ji Zhiyuan Li Yimin Hu University of Science and Technology of China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Chinese Named Entity Recognition (NER) is more complex than in other languages due to the fine granularity of Chinese words. Traditional Chinese NER works have focused on using large amounts of external knowledge to enhance entity detection. However, many professional fields lack sufficient data. To address these shortcomings, we propose an Ordinary Differential Equation Cascaded Fusion Model (ODE-CF) for Chinese NER. In contrast to Large language Models (LLMs) that use text generation models, to take advantage of the embedding information ODE-CF uses a traditional sequence labeling task. At the same time, Compared to the single-tier structure, we employ the two-tier cascaded structure can obtain more Entity Position information (EPI) from the first tier. Furthermore, We propose the ODE-Residual Fusion module to distill the EPI from the output of adjacent blocks in the first tier, which can be utilized in the second tier. Our experiments on the Chinese NER task demonstrate the effectiveness and genericity of this model. It achieves 96.41%, and 70.66% F1 values respectively on the MSRA corpus and Weibo corpus. In particular, further experiments on the small-scale Agris corpus, Youku corpus, and Ecommerce corpus show significant gains that 10.47%, 22.91% and 28.17% higher than the basemodel. It indicates our advantage in data-deficient professional fields, such as agriculture and finance.

关键词： Measurement Large language models Blogs Named entity recognition Ordinary differential equations Transformers Mathematical models Electronic commerce Labeling Periodic structures

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Highly Fast Text Segmentation With Pairwise Markov Chains

Highly Fast Text Segmentation With Pairwise Markov Chains

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international conference on information Science and technology (ICIST)

作者： Elie Azeraf Emmanuel Monfrini Emmanuel Vignon Wojciech Pieczynski SAMOVAR Telecom SudParis Institut Polytechnique de Paris SAMOVAR Telecom SudParis CNRS Institut Polytechnique de Paris Evry France IBM GBS France Bois-Colombes France

ISBN: (数字)9781728166469

ISBN: (纸本)9781728166476

Natural language processing (NLP) models' current trend consists of using increasingly more extra-data to build the best models as possible. It implies more expensive computational costs and training time, difficulties for deployment, and worries about these models' carbon footprint reveal a critical problem in the future. Against this trend, our goal is to develop NLP models requiring no extra-data and minimizing training time. To do so, in this paper, we explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov Chain (PMC), for NLP segmentation tasks. We apply these models for three classic applications: POS Tagging, Named-Entity-Recognition, and Chunking. We develop an original method to adapt these models for text segmentation's specific challenges to obtain relevant performances with very short training and execution times. PMC achieves equivalent results to those obtained by Conditional Random Fields (CRF), one of the most applied models for these tasks when no extra-data are used. Moreover, PMC has training times 30 times shorter than the CRF ones, which validates this model given our objectives.

关键词： Training Adaptation models Computational modeling Hidden Markov models Markov processes Natural language processing Task analysis

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iGrade: an automated short answer grading system 22

iGrade: an automated short answer grading system

引用

Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： Dina Hamed Alhamed Aljawharah Mohammad Alajmi Yasminah Hani Alali Tayma Abdulrahman Alqahtani Maryam Riyadh Alnassar Dina Abdullah Alabbad College of Computer science and Information Technology Imam Abdulrahman Bin Faisal University Saudi Arabia

ISBN: (纸本)9781450397629

During the COVID-19 pandemic, most countries rely on E-Learning to apply social distance policy which affects the exams evaluation process. this project aimed to assist instructors in grading the short answer questions for CCSIT courses. By implanting a website application that the instructors could use to upload the students' answers and the ‘iGrade” software model will grade it. Moreover, the system will reduce the workload on the facilities members by saving time and effort as well as guarantee an objective grading for students. the model used in this project is a state-of-the-art BERT Neural Network model along with layers of BiLSTM that was trained using a dataset that has been collected from previous midterm and final exams of the CIS 211 course. the dataset consists of three categories which are (0, 0.5, 1) with around 1,128 instances. the "iGrade" test obtained an accuracy score of 85,4%, demonstrating BERT's superiority and independence from features during short answer grading as a default method in *** CONCEPTS • Computing methodologies • Artificial intelligence • Natural language processing

关键词： Automated short answer questions grading BERT BiLSTM Deep learning

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Offensive Text Classification based on Ernie??s Dual Channel Composite Model 23

Offensive Text Classification based on Ernie??s Dual Channel...

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Proceedings of the 2023 6th international conference on Machine Learning and Natural language processing

作者： Caizhi Jiang Yutao Li Junkuo Cao School of Information Science and Technology Hainan Normal University China

ISBN: (纸本)9798400709241

With the widespread popularity of the Internet, offensive text information in cyberspace has attracted widespread attention from society. Currently, offensive text recognition mainly relies on pre constructed sensitive words for recognition, which cannot effectively intercept text without obvious offensive words. this article proposes a dual channel composite model based on the Ernie pre training model. First, the Ernie pre training model is used to construct a dynamic word vector, and more efficient text semantic information is obtained through its internal multi-layer and bidirectional Transformer structure. then, a dual channel model is added to further refine text information. Bi-GRU is used to extract global semantics and TextCNN is used to extract local information, extracting semantic information features at different abstract levels. the experimental results show that when tested on the Chinese offensive language dataset COLDataset, the accuracy and F1 value of the model are significantly better than the baseline model COLDetector, reaching 83.81% and 82.90%, respectively. this verifies that adding a dual channel fusion network to the Ernie pre trained model can more effectively extract text features and improve the classification performance of the model.

关键词： Dual channel Ernie pre-trained model Offensive Text

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Sentiment Analysis using a Long Short-Term Memory Recurrent Neural Network on Filipino Tweets: A Case Study for Bank A 23

Sentiment Analysis using a Long Short-Term Memory Recurrent ...

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Proceedings of the 2023 6th international conference on Computational Intelligence and Intelligent Systems

作者： Julio Jeremy Escudero Macrohon Joel C De Goma Julio Jerison Escudero Macrohon School of Information Technology Mapua University Philippines Department of Information Engineering I-Shou University Taiwan

ISBN: (纸本)9798400709067

Social media has emerged as a significant source of big data, offering a vast amount of social data and evidence. However, much of this data remains untapped and underutilized. Nowadays, various organizations, including corporations, government agencies, and data enthusiasts, are actively seeking to extract and leverage this data to create data models that drive actionable insights, helping them achieve their goals. this study focuses on examining customer sentiments towards Bank A, one of the largest banking and financial institutions in the Philippines with a history spanning over 50 years. By employing natural language processing techniques, we aimed to gain a comprehensive understanding of the general sentiment towards the bank's products, services, and overall brand satisfaction. To accomplish this, we annotated the sentiments and utilized them as training and testing data for a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). this model was designed to classify tweets in both English and Filipino languages into two polarities and demonstrated an accuracy rate of 88.50%.

关键词： Filipino Tweets LSTM RNN natural language processing sentiment analysis twitter

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Intelligent Unmanned AI Detection Model for Financial Volatility in Stock Exchange

Intelligent Unmanned AI Detection Model for Financial Volati...

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Fayaz Ahamed Sthitipragyan Biswal Sudhansu Sekhar Nanda Sumit Pundir Tushar Soubhari Saikrishna Boggavarapu Department of Business Administration College of Economics & Business Administration India Department of Finance ASTHA School of Management Bhubaneswar Odisha India Department of Finance & Accounting Kirloskar Institute of Management Yantrapur Karnataka India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Commerce N.S.S. College University of Calicut Manjeri Kerala India Department of Information Technology University of the Cumberlands Prospect Heights IIIioni US

At the present days, the popularity of the stock marketing is very high as this is only the legal platform where the investor can doubling their investments within very short span. However, besides the chance of profitability, there is huge risk present due to the extreme volatility. In this context, to reducing the risk of financial this research study has developed a AI detection model so that a predictive future data related to the stock exchange can be obtained through the past data train. In order to predict where prices will go in the future, this model uses machine learning algorithms and NLP techniques to decipher historical market data. Backtesting on simulated data demonstrates that high levels of accuracy, precision, recall, and Fl score may be attained in the prediction of market volatility using the proposed model. the proposed model may be used by investors and financial institutions as a tool for making more informed investment decisions, reducing risk, and identifying untapped investment opportunities. An important contribution to the field of finance, this research shows how machine learning algorithms and natural language processing methods show potential in predicting market volatility.

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Application downloading 4

Application downloading

引用

4th international Workshop on Real-Time Ada Issues, IRTAW 1990

作者： Balzer, Robert Cooperband, Alvin Feather, Martin London, Philip Wile, David USC Information Sciences Institute 4676 Admiralty.Way Marina del ReyCA90291 United States

ISBN: (纸本)0897911466

the purpose of our research is to investigate the feasibility of this methodology for distributed application development by examining possible approaches to carrying out such developments. In particular, our emphasis lies in transformation technology, wherein user-invoked source-to-source transformations are applied to the partitioned system for the purpose of optimizing the transactions required to effect the distributed behavior. In this way, the application downloading system need only produce a correct partitioning of the original program, not an efficient one. the user, with system guidance, optimizes the partitioned program by applying transformations chosen from the system library. thus, application downloading can be viewed as a narrow domain in which to apply the transformation technology we have developed elsewhere in a broader context(1). © 1981 IEEE Computer Society. All rights reserved.

关键词： Application programs

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Decision-based Batch processing Attack Method

Decision-based Batch Processing Attack Method

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IEEE international conference on Civil Aviation Safety and information technology (ICCASIT)

作者： Rongcheng Duan Kai Cao Zichen Yang Defence Industry Secrecy Examination and Certification Center Beijing China Hangzhou Research Institute Xidian University Hangzhou China China State Shipbuilding Corporation Limited Eighth Research Institute Nanjing China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

Deep Convolutional Neural Networks (CNNs) have significantly impacted various domains, ranging from text recognition to natural language processing, computer vision, and cross-modal sentiment analysis in the community in recent years. Nevertheless, adversarial samples can cause those models to make wrong predictions and pose a huge security threat to production systems and daily life. Studying adversarial attacks exposes the weaknesses of CNNs and highlights the associated security risks, guiding improvements in model robustness. We explored how deep neural networks are vulnerable to adversarial perturbations and highlighted the shortcomings of current computational paradigms. these paradigms are challenged by their inability to produce adversarial instances that are broadly applicable across extensive datasets, thereby limiting the robustness of the models. Despite advances in general adversarial perturbation and SPSA optimization, limitations like high computational demands and sensitivity to model architecture persist. We introduce an enhanced Dec Attack algorithm, incorporating batch accuracy loss and SPSA optimization techniques to tackle this challenge. this enhancement facilitates the efficient execution of adversarial stimuli in decision-based black-box scenarios. the algorithm’s efficacy is evidenced by its superior performance compared to alternative black-box approaches, thereby augmenting the practicality and viability of deploying adversarial strategies.

关键词： Sentiment analysis Accuracy Sensitivity Text recognition Computational modeling Perturbation methods Closed box Robustness Security Optimization

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Vietnamese Text Summarization Based on Elementary Discourse Units 22

Vietnamese Text Summarization Based on Elementary Discourse ...

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Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： Khang Nhut Lam Tai Ngoc Nguyen Jugal Kalita Information Technology Can Tho University Vietnam Computer Science University of Colorado USA

ISBN: (纸本)9781450397629

this paper presents text summarization models based on elementary discourse units (EDUs) to construct extractive and abstractive summarization for Vietnamese documents. First, we introduce algorithms using the POS information for constructing EDUs in Vietnamese. then, the EDUs created are fed into an extractive summarization model using a pointer network and an abstractive summarization model using a pointer generator model. A reinforcement learning method is used to improve the quality of the models. We perform experiments on the CTUNLPSUM dataset, including 1,053,702 Vietnamese documents extracted from online magazines. the extractive summarization models based on EDUs outperform other extractive summarization models based on words or sentences. the ROUGE-1, ROUGE-2, and ROUGE-L of the best extractive and abstractive summarization models are 0.567, 0.241, 0.461; and 0.530, 0.213, 0.394, respectively.

关键词： EDU segmentation Extractive summarization Vietnamese abstractive summarization elementary discourse units reinforcement learning

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Measuring Text-to-SQL Semantic Parsing Model on the Question Generalizability 22

Measuring Text-to-SQL Semantic Parsing Model on the Question...

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Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： thanakrit Julavanich Akiko Aizawa Department of Computer Science Graduate School of Information Science and Technology The University of Tokyo Japan Aizawa Laboratory National Institute of Informatics Japan and Department of Computer Science Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9781450397629

One of the challenges in NLP tasks, such as text-to-SQL semantic parsing, is generalization. In the text-to-SQL task, having separate training and testing data can measure one aspect of the generalization: how well the model generalizes to unseen databases. Other aspects, however, remain unaccounted for. We propose a new dataset and a more challenging and thorough evaluation process that focuses on the two challenges of generalizing the text-to-SQL model: database content references and question patterns. We create SPIDER-QG, an augmented dataset that employs three techniques, to assess generalizability. First, we replace the set of values in the existing test set with other values from the same column in the same database. Second, we use the synonym of each value as a replacement instead. third, we generate new questions for the existing SQL query by back-translating the original question. Our evaluation setup demonstrates the generalization challenges and struggles of the current models.

关键词： datasets model generalizability text-to-SQL

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