[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-490494-105":59,"doc-detail-490494-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","english-hindi-transliteration-using-multiple-similarity-metrics","English-Hindi Transliteration using Multiple Similarity Metrics","","An approach is presented for measuring transliteration similarity between English and Hindi word pairs, treating Devanagari and Roman scripts as mutually mappable. A bi-directional character-level mapping enables transliteration in both directions. A similarity algorithm, derived as a variant of Dice’s coefficient and LCSR, incorporates constraints required for English-Hindi transliterated forms. Evaluation under multiple-measure agreement shows identification of transliteration pairs with up to 0.92 F-measure, and the method is adapted to Gujarati to test portability.",{"@graph":69,"@context":122},[70,84,105],{"@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":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/english-hindi-transliteration-using-multiple-similarity-metrics/490494/",{"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/english-hindi-transliteration-using-multiple-similarity-metrics/490494.png","ImageObject",300,407,{"name":92,"@type":93},"vwvw","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-09","2026-10-01",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the approach in the paper?","Question",{"text":112,"@type":113},"To measure similarity between English-Hindi word pairs and identify when a Hindi word is a transliteration of an English word (and vice-versa).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the method generate candidate transliterations?",{"text":117,"@type":113},"It uses a bi-directional character mapping between Devanagari and the Roman script to transliterate a Hindi word into one or more Roman-script candidate forms, and then scores these candidates by similarity.",{"name":119,"@type":110,"acceptedAnswer":120},"What similarity measure does the paper propose and how is it evaluated?",{"text":121,"@type":113},"It proposes a Transliteration Similarity metric (TSM), a variant combining Dice’s coefficient and LCSR while enforcing transliteration-matching constraints. Various metrics are tested individually and in combinations, and results are reported using an F-measure target up to 0.92.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},490494,1791405255,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":46},3985747874149,"https://ap-avatar.wpscdn.com/avatar/3a001161165b4dc3d9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1790129441039064114","English-Hindi Transliteration using Multiple Similarity Metrics  \nNiraj Aswani, Robert Gaizauskas  \nDepartment of Computer Science  \nUniversity of Sheffield  \nRegent Court, Sheffield, S1 4DP, UK  \n[n.aswani@dcs.shef.ac.uk](n.aswani@dcs.shef.ac.uk), [r.gaizauskas@dcs.shef.ac.uk](r.gaizauskas@dcs.shef.ac.uk)  \nAbstract  \nIn this paper, we present an approach to measure the transliteration similarity of English-Hindi word pairs. Our approach has two components. First we propose a bi-directional mapping between one or more characters in the Devanagari script and one or more characters in the Roman script (pronounced as in English) . This allows a given Hindi word written in Devanagari to be transliterated into the Roman script and vice-versa. Second, we present an algorithm for computing a similarity measure that is a variant of Dice's coefficient measure and the LCSR measure and which also takes into account the constraints needed to match English-Hindi transliterated words. Finally, by evaluating various similarity metrics individually and together under a multiple measure agreement scenario, we show that it is possible to achieve a 0.92 f-measure in identifying English-Hindi word pairs that are transliterations. In order to assess the portability of our approach to other similar languages we adapt our system to the Gujarati language.  \n1. Introduction  \nTransliteration is defined as the task of transcribing a word or text from one writing system into the another writing system such that the pronunciation of the word remains same and a person reading the transcribed word can read it in the original language. Cognates (the words derived from another language) and Named Entities (NE) such as the person names, names of places, organizations are the types of words that need transcribing into the another writing system. In India, English is one of the most popular foreign languages. Following this trend, it is becoming very popular for people to use words from both, the English and the Hindi vocabulary in the same sentence. According to Clair (2002), use of such a mixed language, also known as Hinglish, is said to have prestige, as the amount of mixing corresponds with the level of education and is an indicator of membership in the elite group.  \nAlthough the use of such a mixed code language is becoming very common, the English and the Hindi languages remain widely different in both the structure and the style. According to Rao et al. (2000) these differences can be categorized in two broad categories namely structural differences and style differences. These include differences such as the difference in word order, placements of modifiers, absence of articles and different types of genders in Hindi. For example, in Hindi most of the times verbs are placed at the end of a sentence and postposition are used instead of prepositions. Similarly, whilst the modifiers of an object can occur both before and after the object in English, modifiers only occur before the object they modify in Hindi. In contrast to the English language where there are three genders: masculine, feminine and neuter for pronouns, Hindi  \nhas only two: masculine and feminine.  \nApart from these structural differences there are several other differences in the alphabets of the two languages. For example, the English alphabet has five vowels whereas in Devanagari there are thirteen. The English alphabet has twenty one consonants and the Devanagari has thirty three. There are three compound letters in Devanagari for which  \nthere is no equivalent sound in English. There are certain sounds in English (for example s in pleasure), which are not present in Hindi. It is common to have consonants clusters at the beginning or end of words in English than Hindi which leads to errors in the pronunciation of words such as straight into istraight, fly into faly and film into filam. Such differences can result into an inaccurate transliteration. Fortunately, unlike the Chinese language whic","cbCainYPWVno33O4","https://ap.wps.com/l/cbCainYPWVno33O4","pdf",585944,"English","# Introduction\n## Transliteration and Hinglish context\n## Structural and alphabet differences\n## Transliteration Similarity metric (TSM)\n# Related Work","[{\"question\":\"What is the main goal of the approach in the paper?\",\"answer\":\"To measure similarity between English-Hindi word pairs and identify when a Hindi word is a transliteration of an English word (and vice-versa).\"},{\"question\":\"How does the method generate candidate transliterations?\",\"answer\":\"It uses a bi-directional character mapping between Devanagari and the Roman script to transliterate a Hindi word into one or more Roman-script candidate forms, and then scores these candidates by similarity.\"},{\"question\":\"What similarity measure does the paper propose and how is it evaluated?\",\"answer\":\"It proposes a Transliteration Similarity metric (TSM), a variant combining Dice’s coefficient and LCSR while enforcing transliteration-matching constraints. Various metrics are tested individually and in combinations, and results are reported using an F-measure target up to 0.92.\"}]","English-Hindi Transliteration using Multiple Similarity Metrics | PDF",1790838384]