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The thesis develops a system that generates such analogies automatically from a corpus, treating generation as three steps: identifying semantically related word pairs, extracting relation-characterizing clues, and generating candidate analogies by feature matching. A dependency-grammar-based representation combined with machine learning determines relational similarity, achieving precision above 90% in empirical evaluation, with applications to text understanding and learning ontological relations.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/from-atoms-to-the-solar-system-generating-lexical-analogies-from-text/267354/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/from-atoms-to-the-solar-system-generating-lexical-analogies-from-text/267354.png","ImageObject",300,407,{"name":92,"@type":93},"Grenda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-16","2026-09-14",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is a lexical analogy and why is it useful?","Question",{"text":113,"@type":114},"A lexical analogy links two word pairs whose underlying relations are identical or highly similar. It offers a relational similarity measure and supports tasks such as text understanding and learning ontological relations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the thesis system generate lexical analogies from text?",{"text":118,"@type":114},"The system processes text in three stages: it identifies semantically related word pairs, finds clues that characterize their relations, and generates analogies by matching pairs with similar relations.",{"name":120,"@type":111,"acceptedAnswer":121},"What methods does the system use to model semantic relations and measure similarity?",{"text":122,"@type":114},"It uses a dependency grammar to characterize semantic relations and applies machine learning to determine similarities between those relations.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},267354,1789417528,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971474921005,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","From Atoms to the Solar System: Generating Lexical Analogies from Text  \nby  \nPei-Wen Andy Chiu  \nA thesis  \npresented to the University of Waterloo in ful􀀌lment of the thesis requirement for the degree of Master of Mathematics  \nin Computer Science  \nWaterloo, Ontario, Canada, 2006  \n􀀍c Andy Chiu 2006  \nI hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required 􀀌nal revisions, as accepted by my examiners.  \nI understand that my thesis may be made electronically available to the public.  \nAbstract  \nA lexical analogy is two pairs of words (w1 , w2 ) and (w3 , w4 ) such that the relation between w 1 and w2 is identical or similar to the relation between w3 and w4 . For example,(abbreviation, word ) forms a lexical analogy with (abstract, report ), because in both cases the former is a shortened version of the latter. Lexical analogies are of theoretic interest because they represent a second order similarity measure: relational similarity. Lexical analogies are also of practical importance in many applications, including textunderstanding and learning ontological relations.  \nThis thesis presents a novel system that generates lexical analogies from a corpus of text documents. The system is motivated by a well-established theory of analogymaking, and views lexical analogy generation as a series of three processes: identifying pairs of words that are semantically related, 􀀌nding clues to characterize their relations, and generating lexical analogies by matching pairs of words with similar relations. The system uses a dependency grammar to characterize semantic relations, and applies machine learning techniques to determine their similarities. Empirical evaluation shows that the system performs remarkably well, generating lexical analogies at a precision of over 90% .  \nAcknowledgments  \nI would like to thank the following people, without whom this thesis would not have been possible.  \n... my supervisors Pascal Poupart and Chrysanne DiMarco for their guidance throughout this research.  \n... my readers Frank Tompa and Fakhri Karray for their invaluable comments and suggestions.  \n... my parents Louis and Shirley, sister Betty, brother-in-law Terry, and nephew Joshua, for their never-ending support and encouragement.  \n... my wife Cybill for her sel􀀍ess sacri􀀌ce to always be there for me.  \nAbove all, I would like to thank God, for all of the above.  \nContents  \n1 Introduction 1  \n1.1 Analogies Everywhere ............................ 1  \n1.2 Lexical Analogies ............................... 2  \n1.3 Lexical Analogy Generation ......................... 3  \n1.4 Research Contributions ............................ 4  \n1.5 Thesis Organization .............................. 5  \n2 Related Work 6  \n2.1 Computational Models of Analogy-Making ................. 6  \n2.2 Lexical Analogy Comparison ......................... 8  \n2.3 Lexical Analogy Generation ......................... 12  \n3 GELATI-Theory and Intuitions 16  \n3.1 Theoretical Grounding ............................ 16  \n3.2 Objects: Semantically-Related Word-Pairs ................. 17  \n3.3 Structure: Word-Pair Features ........................ 18  \n3.4 Analogy: Feature Matching ......................... 19  \n4 GELATI-Implementation 20  \n4.1 Dependency Grammar ............................ 20  \n4.2 GELATI Overview .............................. 22  \n4.3 Preprocessor .................................. 22  \n4.4 Word-Pair Extractor ............................. 24  \n4.5 Feature Extractor ............................... 27  \n4.6 Extraction Filter ............................... 28  \n4.7 Analogy Generator .............................. 30  \n4.8 Analogy Filter ................................. 36  \n5 Experimental Evaluation 37  \n5.1 Experimental Setup .............................. 37  \n5.2 Experimental Results ............................. 40  \n5.3 Discussions .................................. 46  \n6 Conclusion 51  \n6.1 Summary ...","cbCailMcL3C6cWr3","https://ap.wps.com/l/cbCailMcL3C6cWr3","pdf",1245807,68,"English","# Introduction\n## Analogies Everywhere\n## Lexical Analogy Generation\n## Research Contributions\n# Related Work\n## Computational Models of Analogy-Making\n## Lexical Analogy Comparison\n## Lexical Analogy Generation\n# GELATI-Theory and Intuitions\n## Theoretical Grounding\n## Objects: Semantically-Related Word-Pairs\n## Structure: Word-Pair Features\n## Analogy: Feature Matching\n# GELATI-Implementation\n## Dependency Grammar\n## GELATI Overview\n## Preprocessor\n## Word-Pair Extractor\n## Feature Extractor\n## Extraction Filter\n## Analogy Generator\n## Analogy Filter\n# Experimental Evaluation\n## Experimental Setup\n## Experimental Results\n## Discussions\n# Conclusion\n## Summary\n## Future Directions\n## Learning Ontological Relations","[{\"question\":\"What is a lexical analogy and why is it useful?\",\"answer\":\"A lexical analogy links two word pairs whose underlying relations are identical or highly similar. It offers a relational similarity measure and supports tasks such as text understanding and learning ontological relations.\"},{\"question\":\"How does the thesis system generate lexical analogies from text?\",\"answer\":\"The system processes text in three stages: it identifies semantically related word pairs, finds clues that characterize their relations, and generates analogies by matching pairs with similar relations.\"},{\"question\":\"What methods does the system use to model semantic relations and measure similarity?\",\"answer\":\"It uses a dependency grammar to characterize semantic relations and applies machine learning to determine similarities between those relations.\"}]","From Atoms to the Solar System - Generating Lexical Analogies from Text | PDF",171]