[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-204335-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-204335-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","simple-ppdb-paraphrase-database-for-text-simplication","Simple PPDB - Paraphrase Database for Text Simplification","","The document introduces Simple Paraphrase Database (Simple PPDB), a text-simplification-oriented subset of the larger Paraphrase Database (PPDB). A supervised model assigns simplification scores to phrase pairs, enabling ranked outputs competitive with lexical simplification approaches. Simple PPDB provides 4.5 million paraphrase rules and is described as the largest available resource for lexical simplification. The work supports broader research into advanced end-to-end simplification using phrasal rule coverage.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/simple-ppdb-paraphrase-database-for-text-simplication/204335/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/simple-ppdb-paraphrase-database-for-text-simplication/204335.png","ImageObject",300,407,{"name":42,"@type":43},"Chloe Bennett","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-05","2026-09-05",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",13,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is Simple PPDB and how is it related to PPDB?","Question",{"text":63,"@type":64},"Simple PPDB is a subset of the Paraphrase Database (PPDB) adapted specifically for text simplification. It focuses on lexical and phrasal paraphrase rules that can simplify input while preserving meaning.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the proposed model determine which paraphrase rules simplify and preserve meaning?",{"text":68,"@type":64},"The approach trains a supervised model to distinguish rules that simplify the input, rules that make it less simple, and rules that produce bad paraphrases. This is paired with simplification scoring used to rank candidate phrase pairs.",{"name":70,"@type":61,"acceptedAnswer":71},"What size and scope does the Simple PPDB resource provide?",{"text":72,"@type":64},"Simple PPDB contains 4.5 million simplifying paraphrase rules. After preprocessing, the work also describes a set of 7.5 million paraphrase rules covering 625K unique lemmatized words and phrases.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},204335,1788572592,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":102,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},962084925782,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Simple PPDB: A Paraphrase Database for Simplification  \nEllie Pavlick  \nUniversity of Pennsylvania [epavlick@seas.upenn.edu](epavlick@seas.upenn.edu)  \nChris Callison-Burch  \nUniversity of Pennsylvania [ccb@cis.upenn.edu](ccb@cis.upenn.edu)  \nAbstract  \nWe release the Simple Paraphrase Database, a subset of of the Paraphrase Database (PPDB) adapted for the task of text simplification. We train a supervised model to associate simplification scores with each phrase pair, producing rankings competitive with state-of-theart lexical simplification models. Our new simplification database contains  \n4.5 million paraphrase rules, making it the largest available resource for lexical simplification.  \n1 Motivation  \nLanguage is complex, and the process of reading and understanding language is difficult for many groups of people. The goal of text simplification is to rewrite text in order to make it easier to understand, for example, by children (De Belder and Moens, 2010), language learners (Petersen and Ostendorf, 2007), people with disabilities (Rello et al., 2013; Evans et al., 2014), and even by machines (Siddharthan et al., 2004) . Automatic text simplification (Napoles and Dredze, 2010; Wubben et al., 2012; Xu et al., 2016) has the potential to dramatically increase access to information by making written documents available at all reading levels.  \nFull text simplification involves many steps, including grammatical restructuring and summarization (Feng, 2008) . One of the most basic subtasks is lexical simplification (Specia et al., 2012)– replacing complicated words and phrases with simpler paraphrases. While there is active research in the area of lexical simplification (Costerand Kauchak, 2011a; Glava and ˇStajner, 2015; Paetzold, 2015), existing models have been byand-large limited to single words. Often, how-  \nmedical practitioner ! doctor  \nlegislative texts ! laws  \nhypertension ! high blood pressure  \nprevalent ! very common  \nsignificant quantity ! a lot impact negatively ! be bad  \nTable 1: In lexical simplification, it is often necessary to replace single words with phrases or phrases with single words. The above are examples of such lexical simplifications captured by the Simple PPDB resource.  \never, it is preferable, or even necessary to paraphrase a single complex word with multiple simpler words, or to paraphrase multiple words with a single word. For example, it is difficult to imagine a simple, single-word paraphrase of hypertension, but the three-word phrase high blood pressure is avery good simplification (Table 1) . Such phrasal simplifications are overlooked by current lexical simplification models, and thus are often unavailable to the end-to-end text simplification systems that require them.  \nRecent research in data-driven paraphrasing has produced enormous resources containing millions of meaning-equivalent phrases (Ganitkevitch et al., 2013) . Such resources capture a wide range of language variation, including the types of lexical and phrasal simplifications just described. In this work, we apply state-of-the-art machine learned models for lexical simplification in order to identify phrase pairs from the Paraphrase Database (PPDB) applicable to the task of text simplification. We introduce Simple PPDB, 1 a subset of the Paraphrase Database containing 4.5 million simplifying paraphrase rules. The large scale of Simple PPDB will support research into increasingly advanced methods for text simplification.  \n1 [http://www.seas.upenn.edu/](http://www.seas.upenn.edu/) ˜nlp/ resources/simple-ppdb .tgz  \n143  \nProceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 143–148, Berlin, Germany, August 7-12, 2016 . 􀀍c2016 Association for Computational Linguistics  \n2 Identifying Simplification Rules  \n2.1 Paraphrase Rules  \nThe Paraphrase Database (PPDB) is currently the largest available collection of paraphrases. Each paraphrase rule in the database has an automatically-assigned quality score betwe","cbCaiaQj45duJwZX","https://ap.wps.com/l/cbCaiaQj45duJwZX","pdf",127599,"English","# Abstract\n# Motivation\n# Identifying Simplification Rules\n## Paraphrase Rules\n## Lexical Simplification Model","[{\"question\":\"What is Simple PPDB and how is it related to PPDB?\",\"answer\":\"Simple PPDB is a subset of the Paraphrase Database (PPDB) adapted specifically for text simplification. It focuses on lexical and phrasal paraphrase rules that can simplify input while preserving meaning.\"},{\"question\":\"How does the proposed model determine which paraphrase rules simplify and preserve meaning?\",\"answer\":\"The approach trains a supervised model to distinguish rules that simplify the input, rules that make it less simple, and rules that produce bad paraphrases. This is paired with simplification scoring used to rank candidate phrase pairs.\"},{\"question\":\"What size and scope does the Simple PPDB resource provide?\",\"answer\":\"Simple PPDB contains 4.5 million simplifying paraphrase rules. After preprocessing, the work also describes a set of 7.5 million paraphrase rules covering 625K unique lemmatized words and phrases.\"}]","Simple PPDB - Paraphrase Database for Text Simplification | PDF",15]