[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124748-en":3,"doc-seo-124748-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124748,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","A Machine Learning Approach for Generating a Recursive Object Model from a Natural Language Text","This research investigates machine learning algorithms as an alternative to rule-based systems for generating Recursive Object Model (ROM) diagrams from natural language text. It addresses limitations in rule-based approaches by developing software to collect labeled data for a supervised learning workflow. The proposed model combines Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) networks, taking word pairs and sentences to classify relations. Evaluation compares the model with a baseline, tests generalization on unseen data, and analyzes how it mitigates rule-based constraints, showing superior accuracy.","A Machine Learning Approach for Generating a Recursive Object Model from a Natural Language Text  \nAmin Bayatpour  \nA Thesis  \nin  \nThe Department  \nof  \nConcorida Institute for Information Systems Engineering  \nPresented in Partial Fulfillment of the Requirements  \nfor the Degree of  \nMaster of Applied Science (Quality Systems Engineering) at  \nConcordia University  \nMontral, Qubec, Canada  \nAugust 2023  \n© Amin Bayatpour, 2023  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared  \nBy: Amin Bayatpour  \nEntitled: A Machine Learning Approach for Generating a Recursive Object  \nModel from a Natural Language Text  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Applied Science (Quality Systems Engineering)  \ncomplies with the regulations of this University and meets the accepted standards with respect to originality and quality.  \nSigned by the Final Examining Committee:  \n  Chair  \nDr. Jun Yan  \n  External Examiner Dr. Mazdak Nik-bakht  \n  Examiner  \nDr. Jun Yan  \nDr. Yong Zeng  Supervisor  \nApproved by  Dr. Chun Wang, Director  Department of Concorida Institute for Information Systems Engineering  \nAugust 2023  Dr. Mourad Debbabi, Dean  Faculty of Engineering and Computer Science  \nAbstract  \nA Machine Learning Approach for Generating a Recursive Object Model from a Natural  \nLanguage Text  \nAmin Bayatpour  \nThis research investigates the potential of machine learning algorithms as an alternative approach to rule-based systems for generating Recursive Object Model (ROM) diagrams. The existing rule-based approach suffers from limitations and challenges, and this study aims to explore the possibility of overcoming these limitations by leveraging machine learning techniques. To achieve the research objectives, software was developed to gather labelled data for our supervised learning problem. A model comprised of Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models was created and trained using the labelled data. The proposed model takes a pair of words and a sentence as inputs and classifies the appropriate relations among the pairs. Subsequently, a comprehensive evaluation was conducted to assess the effectiveness of the proposed model. The evaluation process involved a comparative analysis between the proposed model and a baseline model, an evaluation of the proposed model on unseen data, and an investigation into the capability of the design model in addressing the limitations of the rule-based system. The evaluation results demonstrate the superiority of the proposed model. Firstly, the proposed model achieved an exceptional accuracy of 97 percent in the training process, surpassing the baseline model’s accuracy of approximately 61 percent. Secondly, the proposed model exhibited an accuracy of 96 percent on unseen data, thus showcasing its ability to generalize effectively to new instances. Lastly, when comparing the proposed intelligent system with the rule-based system, although the proposed methodology exhibited minor errors in generating ROM diagrams for certain scenarios, the findings underscore the potential of the proposed model in mitigating the limitations of the rule-based  \nsystem.  \nAcknowledgments  \nI would like to take this opportunity to express my deep and sincere appreciation to my respected supervisor, Dr. Yong Zeng. His constant support and invaluable guidance have been a guiding light throughout my academic journey. His expert insights and continuous encouragement have not only enhanced my understanding of the subject but have also played a significant role in shaping me as a learner and an individual.  \nI am also immensely grateful to my family and friends who have been unwavering pillars of strength and motivation throughout these two remarkable years. Their constant presence and words of encouragement have made this academic pursuit all the more meaningful and enjoyable. Their belief in me has fueled my ","cbCaiqzPHwp2yho8","https://ap.wps.com/l/cbCaiqzPHwp2yho8","pdf",6254359,1,87,"English","en",105,"# 1 Introduction\n## 1.1 Motivation\n## 1.2 Problem Statement\n## 1.3 Research Objective\n## 1.4 Outline\n# 2 Literature Review\n## 2.1 Introduction\n## 2.2 Environment Based Design and Recursive Object Model\n## 2.3 Natural Language Processing\n## 2.4 Machine Learning in Natural Language Processing\n# 3 Methodology\n## 3.1 Introduction\n## 3.2 Research Methodology","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To explore machine learning techniques for generating Recursive Object Model (ROM) diagrams from natural language text as an alternative to rule-based systems.\"},{\"question\":\"How does the proposed system learn from data?\",\"answer\":\"It develops software to gather labeled data for supervised learning, then trains a model built from MLP and LSTM to classify the appropriate relations among word pairs.\"},{\"question\":\"What do the evaluation results show compared with the baseline and rule-based approaches?\",\"answer\":\"The proposed model achieves about 97% accuracy in training and 96% on unseen data, outperforming a baseline around 61%. Compared with rule-based generation, it may make minor errors in some scenarios but demonstrates better capability in mitigating rule-based limitations.\"}]","A Machine Learning Approach for Generating a Recursive Object Model from a Natural Language Text | PDF",1785894270,219,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-for-generating-a-recursive-object-model-from-a-natural-language-text","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-for-generating-a-recursive-object-model-from-a-natural-language-text/124748/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the research?","Question",{"text":75,"@type":76},"To explore machine learning techniques for generating Recursive Object Model (ROM) diagrams from natural language text as an alternative to rule-based systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed system learn from data?",{"text":80,"@type":76},"It develops software to gather labeled data for supervised learning, then trains a model built from MLP and LSTM to classify the appropriate relations among word pairs.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the evaluation results show compared with the baseline and rule-based approaches?",{"text":84,"@type":76},"The proposed model achieves about 97% accuracy in training and 96% on unseen data, outperforming a baseline around 61%. Compared with rule-based generation, it may make minor errors in some scenarios but demonstrates better capability in mitigating rule-based limitations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]