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检索条件"任意字段=7th International Conference on Advanced Language Processing and Web Information Technology"

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Extracting Structured Data from Unstructured Text Using Conditional Random Field and Jaccard Similarity 2019

Extracting Structured Data from Unstructured Text Using Cond...

引用

Proceedings of the 2019 7th international conference on information technology: IoT and Smart City

作者： Sarun Wiriyapistan Sukree Sinthupinyo Department of Computer Engineer Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9781450376631

Nowadays, purchasing information is usually found in form of formal messages such as an email. However, most messages are written in natural language form which is difficult to extract data. Our approach paper uses Conditional Random Fields and Words Similarity to extract data from customers' purchase order emails. We started from dividing the words in the email into a sequence of words. then, we added features of each word. After that, we establish an appropriate template, characteristics and training sample sequences. Next, purchasing information was used for text extraction. From our experiment, it was found that adapting Words Similarity with Conditional Random fields can enhance the accuracy of extracting data significantly.

关键词： Unstructured Text Text Similarity Conditional Random Fields Natural language processing Jaccard Similarity

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Framework for Design and Implementation of Chat Support System using Natural language processing

Framework for Design and Implementation of Chat Support Syst...

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Intelligent Engineering and Management (ICIEM), international conference on

作者： P. William Govinda Rajulu Lanke Venkata Narasimha Rao Inukollu Prabhdeep Singh Anurag Shrivastava Rohit Kumar Department of Information Technology Sanjivani College of Engineering Savitribai Phule Pune University Pune ResMed Digital Health Technology USA Purdue University Fortwayne Indiana USA Department of CSE Graphic Era Deemed Graphic Era Hill University Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Humanities Lloyd Institute of Engineering and Technology Greater Noida

Companies have increasingly started adopting chatbots as a customer service support system to provide more personalized service experience to customers. this technology is catching attention in the customer service context because chatbots can support multiple customers at one, have the ability to provide multi-lingual support, they are available $24^*7$ with no extra cost, relieves humans from repetitive task and can respond to a variety of troubleshooting queries without any latency. this research paper aims at developing a customer support chatbot called ‘Anjore’ for the Chhattisgarh State Power Distribution Company Limited. the word Anjore (pronounced as $\Lambda \mathrm{n}.\mathrm{d}_3\mathrm{o}:\mathrm{r}$ ) is derived from chhattisgarhi language which means ‘light’. the chatbot is developed in django web frame work and spaCy NLP library for Python has been employed to understand utterances in English language.

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Learning tool of chemical molecules for healthcare students based on 3D visualizations, Large language Models and Linked Open Data 23

Learning tool of chemical molecules for healthcare students ...

引用

Proceedings of the 2023 7th international conference on Education and E-Learning

作者： Maria Lorena Prieto Maria Fuencisla Gilsanz Enrique Puertas Universidad Europea de Madrid Espana

ISBN: (纸本)9798400708732

this paper presents a software tool designed to assist healthcare students and teachers in applying case-based learning methodologies for studying chemical compounds and their structures in a three-dimensional (3D) space. the tool utilizes natural language processing algorithms based on Large language Models (LLM) to extract chemical entities from biomedical text documents. the tool provides students with a starting point for their research in a case-based learning exercise, and by using Large language Models and Linked Open Data, it shows them a path to follow. the system shows a description of the molecules, a 3D view, a 2D structure information, and a panel with scientific articles about the selected chemical compound obtained from the Scielo portal, a repository of scientific publications. the application has a modular architecture that allows for easy addition of new functionalities in the future. the web interface let users to input the text of the clinical case for processing and studying. the functionality of querying scientific articles can be expanded with new modules to obtain information from other repositories. Overall, this software tool provides a comprehensive and innovative approach to learning chemical molecules for healthcare students, which can help them develop the necessary skills to tackle real-life problems as healthcare professionals.

关键词： artificial intelligence chatbot e-learning large language models learning bots learning management system

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Implementation of Semantic-Based Approach Against Frequency & Graph-Based Approach for Concise Human Summarization

Implementation of Semantic-Based Approach Against Frequency ...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Sampath Alankritha Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

the volume of data generating by organizations and individuals is significantly increasing daily. the surge in data generation is by technological advancements, widespread usage of Social Media platforms, and the increasing Digitization of various industries incorporating the healthcare, corporate, and business sectors. However, this data explosion has left both organizations and individuals challenged in the consumption of time for searching relevant information over the internet. To address this problem, advanced technologies such as Natural language processing (NLP) have emerged, focusing on facilitating seamless human interaction with Computers in natural language by leveraging sophisticated algorithms and techniques. the proposed study of this research paper focuses on comparisons of NLP-Frequency and Graph-based Extractive Text Summarization algorithms with Sematic approaches results. To generate an automated system capable of generating human-like concise summaries from various web pages/ Research papers/ entertainment & educational blogs. the primary data source/dataset required for this model is the web URLs obtained from a search engine such as Google, Bing, etc. this system streamlines and automates the process by simply clicking on a URL, offering a standardized and efficient approach to accessing the summarized information. It caters to a wide range of users like research scholars, students, news article readers, and general internet users. To further enhance the user experience, the summarized information is read by the Speech Assistor to reduce the manual efforts/dependencies for searching over the internet, visiting webpages, excessive clicks, and multiple scrolling of webpages. It also standardizes/automates the processes by displaying the top links of the Google search engine by the speech assistor.

关键词： Uniform resource locators Social networking (online) Standards organizations Organizations Search engines Speech enhancement Natural language processing

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Research on Translation of Cross-border E-commerce websites in China

Research on Translation of Cross-border E-commerce Websites ...

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2018 7th international conference on information,Communication and Education Application (ICEA 2018)

作者： Lu Hu Chengdu University of Information Technology

this document analyzes the translation status quo of cross-border E-commerce websites in China. It points out two major existing problems in the translation, which are incorrect choice of words and lack of Intercultural communication. the paper also offers some suggestion for further improvement in the translation of cross-border E-commerce websites in China: products are not only to be domestically but also internationally oriented, which requires enterprises to build up and strengthen their cross-border consciousness and focus on sustainable English website development;professional translation teams are supposed to be built to satisfy the specific needs of dynamic translation.

关键词： Cross-border E-commerce websites Translation Improvement

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Improving Academic Skills Assessment with NLP and Ensemble Learning

Improving Academic Skills Assessment with NLP and Ensemble L...

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international conference on information Systems and Computer Aided Education (ICISCAE)

作者： Xinyi Huang Yingyi Wu Danyang Zhang Jiacheng Hu Yujian Long University of Chicago Chicago USA Rensselaer Polytechnic Institute Seattle USA San Jose State University San Jose USA Tulane University New Orleans USA Independent Researcher TX USA

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

this study addresses the critical challenges of assessing foundational academic skills by leveraging advancements in natural language processing (NLP). Traditional assessment methods often struggle to provide timely and comprehensive feedback on key cognitive and linguistic aspects, such as co-herence, syntax, and analytical reasoning. Our approach inte-grates multiple state-of-the-art NLP models, including BERT, RoBERTa, BART, DeBERTa, and T5, within an ensemble learning framework. these models are combined through stacking techniques using LightGBM and Ridge regression to enhance predictive accuracy. the methodology involves detailed data preprocessing, feature extraction, and pseudo-label learning to optimize model performance. By incorporating sophisticated NLP techniques and ensemble learning, this study significantly improves the accuracy and efficiency of assessments, offering a robust solution that surpasses traditional methods and opens new avenues for educational technology research focused on enhancing core academic competencies.

关键词： Measurement Accuracy Text categorization Stacking Syntactics Predictive models Linguistics Natural language processing Ensemble learning information systems

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Automated Filipino language Treebank Generator 23

Automated Filipino Language Treebank Generator

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Proceedings of the 2023 7th international conference on Natural language processing and information Retrieval

作者： Janelle Kyra A. Sagum Ria Ambrocio Sagum Michael B. Dela Fuente Carlo G. Inovero Department of Information Technology College of Computer and Information Sciences Poytechnic University of the Philippines Philippines Research Management Office Poytechnic University of the Philippines Philippines and Department of Computer Science College of Computer and Information Sciences Poytechnic University of the Philippines Philippines Department of Computer Science College of Computer and Information Sciences Poytechnic University of the Philippines Philippines

ISBN: (纸本)9798400709227

Filipino is a changing language that poses several challenges. the study was done to overcome the time needed to construct a corpus with it syntactic structure, treebank, as a part of the project where the researchers’ part is for Filipino language Treebank. this project is, Open Collaboration for Developing and Using Asian language Treebank (ALT). the objective of this study is to parse Tagalog text and determine grammar rules and part-of-speech tags using a Probabilistic Context-free Grammar. the model was trained and tested using the Asian language Treebank Filipino dataset which includes Filipino sentences and their grammar rules. the system achieved 86.43% f-measure score in parsing the grammar rules and part-of-speech tags of Filipino sentences. the model can reliably be used to automatically create treebanks for Filipino sentence grammar.

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Transfer Learning in Natural language processing

Transfer Learning in Natural Language Processing

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international conference on information and Communications technology (ICOIACT)

作者： Shenson Joseph Ashkok Gadi Parthi Durgaraman Maruthavanan Vivekananda Jayaram Prema Kumar Veerapaneni Vidyasagar Parlapalli University Of North Dakota USA

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

Transfer learning has been a game-changer for natural language processing (NLP), and this technique has massively accelerated progress in the field of NLP, specifically by substantially benefiting from advancements being made elsewhere. We evaluated their performance economic model on a set of tasks and found they outperformed generic transformer models in accuracy as well as generalization. this is also a reminder of the importance of large-scale pretraining, which requires less task-specific data and consequently facilitates faster model development. To achieve a better understanding of the impact on model performance from fine-tuning, combined with datasets characteristics and architecture details. they demonstrated that transfer learning models not only increase classification accuracy in various fields, ranging from healthcare to legal document analysis but also vastly improve the efficiency. these results have important implications, they extend transfer learning from being merely a trend in NLP to something truly necessary for making progress on several fundamental task of NLP. However, work is not done on model interpretability and extending these improvements to low resource languages. this suggests future research in operationalizing these powerful tools transparently and fairly across a wide range of linguistic and cultural contexts. We hope that it can help, at least in some way, to grow the community interested on learning more about how these models work so effectively and guide them toward using transfer learning wherever valuable.

关键词： Analytical models Adaptation models Sentiment analysis Accuracy Transfer learning Text categorization Transformers Natural language processing Data models Question answering (information retrieval)

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Extraction of Complex Sentence Relationships Based on Formalized Chinese Clause Structures

Extraction of Complex Sentence Relationships Based on Formal...

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Machine Learning and Natural language processing (MLNLP), international conference on

作者： Wen Hao School of Information and Control Engineering Xi'an University of Architecture Technology Xi'an China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

In the field of relation extraction (RE) from text, English clause structures have recently emerged as a promising method with significant practical value. However, applying this approach to Chinese clauses presents challenges due to the absence of formal structural representation. Our research shows that Chinese clauses follow a conditional probability function of the core relation P, which can be expressed as C = (S|P, O|P, SO|P), demonstrating a formal structural characteristic of discrete complementary synthesis. Notably, the O|P structure, with a high conditional probability, accounts for 79% of cases, serving as the core of complex sentence relations in Chinese. this sharply contrasts with the structural features of English clauses. Building on this, we propose the Discrete Complementary Synthesis Formal Structure (DCSFS) clause unit model. this model is employed to analyze and verify the role of the clause unit as the fundamental encoding element in Chinese sentences. Accordingly, we develop a method for automatically extracting semantic relations from complex Chinese sentences using this formalized clause unit model. Experimental results show that our approach outperforms existing punctuation-based clause determination methods and effectively addresses the challenges of extracting complex sentence relations from Chinese texts.

关键词： Analytical models Semantics Buildings Machine learning Probability Feature extraction Natural language processing Encoding

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Risk Management Using Big Real Time Data

Risk Management Using Big Real Time Data

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IEEE international conference on Cloud Computing technology and Science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. this thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. the results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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