{"id":"https://openalex.org/W4384625568","doi":"https://doi.org/10.1145/3539618.3591695","title":"Unsupervised Readability Assessment via Learning from Weak Readability Signals","display_name":"Unsupervised Readability Assessment via Learning from Weak Readability Signals","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384625568","doi":"https://doi.org/10.1145/3539618.3591695"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3591695","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111226576","display_name":"Yuliang Liu","orcid":"https://orcid.org/0000-0001-7165-4341"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yuliang Liu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-7165-4341","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082553860","display_name":"Zhiwei Jiang","orcid":"https://orcid.org/0000-0001-5243-4992"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwei Jiang","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-5243-4992","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087888694","display_name":"Yafeng Yin","orcid":"https://orcid.org/0000-0002-9497-6244"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yafeng Yin","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-9497-6244","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015574942","display_name":"Cong Wang","orcid":"https://orcid.org/0000-0003-0916-7803"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cong Wang","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0003-0916-7803","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102937893","display_name":"Sheng Chen","orcid":"https://orcid.org/0009-0005-1342-5985"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Chen","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0005-1342-5985","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013289027","display_name":"Zhaoling Chen","orcid":"https://orcid.org/0009-0000-0041-5255"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaoling Chen","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0000-0041-5255","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061025205","display_name":"Qing Gu","orcid":"https://orcid.org/0000-0002-1112-790X"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Gu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-1112-790X","affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5111226576"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07675526,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1324","last_page":"1334"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13629","display_name":"Text Readability and Simplification","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13629","display_name":"Text Readability and Simplification","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9811000227928162,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9616000056266785,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/readability","display_name":"Readability","score":0.9509372711181641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7911977171897888},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.7104747295379639},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6578593850135803},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.6271467208862305},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5461652278900146},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5140377283096313},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5081775188446045},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5025618076324463},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.47034862637519836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34608644247055054},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3438398838043213},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06904423236846924}],"concepts":[{"id":"https://openalex.org/C2778143727","wikidata":"https://www.wikidata.org/wiki/Q1820650","display_name":"Readability","level":2,"score":0.9509372711181641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7911977171897888},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7104747295379639},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6578593850135803},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6271467208862305},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5461652278900146},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5140377283096313},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5081775188446045},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5025618076324463},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.47034862637519836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34608644247055054},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3438398838043213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06904423236846924},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539618.3591695","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3591695","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8899999856948853}],"awards":[{"id":"https://openalex.org/G5612877841","display_name":null,"funder_award_id":"61972192, 62172208, 61906085, 41972111","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1507658324","https://openalex.org/W1746111881","https://openalex.org/W1979691036","https://openalex.org/W2048587526","https://openalex.org/W2155548281","https://openalex.org/W2250436894","https://openalex.org/W2483327705","https://openalex.org/W2514166524","https://openalex.org/W2740887992","https://openalex.org/W2806183494","https://openalex.org/W2916535003","https://openalex.org/W2953203476","https://openalex.org/W2964941017","https://openalex.org/W3034819089","https://openalex.org/W3037554857","https://openalex.org/W3043412608","https://openalex.org/W3099585677","https://openalex.org/W3134409176","https://openalex.org/W3202780679","https://openalex.org/W4200064278","https://openalex.org/W4285122665","https://openalex.org/W4285299306","https://openalex.org/W4287890115","https://openalex.org/W4297902813","https://openalex.org/W4310609309","https://openalex.org/W4327498744"],"related_works":["https://openalex.org/W1964661231","https://openalex.org/W4254960163","https://openalex.org/W3110264473","https://openalex.org/W2032810564","https://openalex.org/W2370831213","https://openalex.org/W2011472225","https://openalex.org/W2767338541","https://openalex.org/W3000057026","https://openalex.org/W3048565508","https://openalex.org/W3163984363"],"abstract_inverted_index":{"Unsupervised":[0],"readability":[1,38,76,86,166,179],"assessment":[2,180],"aims":[3],"to":[4,35,48,80,91,129,217],"evaluate":[5],"the":[6,24,33,82,109,125,131,141,147,158,168],"reading":[7],"difficulty":[8],"of":[9,26,59,68,151,170],"text":[10,75],"without":[11],"any":[12],"manually-labeled":[13],"data":[14,28],"for":[15,32,88,98,232],"model":[16,34,52,83,99,106],"training.":[17],"This":[18,154],"is":[19,162],"a":[20,45,50,66,103],"challenging":[21],"task":[22],"because":[23],"absence":[25,169],"labeled":[27,62],"makes":[29],"it":[30],"difficult":[31],"understand":[36],"what":[37],"is.":[39],"In":[40],"this":[41],"paper,":[42],"we":[43,101,138],"propose":[44,139],"novel":[46],"framework":[47],"Learn":[49],"neural":[51],"from":[53,77,112],"Weak":[54],"Readability":[55],"Signals":[56],"(LWRS).":[57],"Instead":[58],"relying":[60],"on":[61,117,146,176,210],"data,":[63],"LWRS":[64,188,208],"utilizes":[65],"set":[67],"heuristic":[69,95,191],"signals":[70,97],"that":[71,107,161,186],"specialize":[72],"in":[73,84,167,222],"describing":[74],"different":[78],"aspects":[79,115],"guide":[81],"outputting":[85],"scores":[87],"ranking.":[89],"Specifically,":[90],"effectively":[92,215],"use":[93],"multiple":[94,113],"weak":[96],"training,":[100],"build":[102],"multi-signal":[104],"learning":[105],"ranks":[108],"unlabeled":[110],"texts":[111],"readability-related":[114],"based":[116,145],"intra-":[118],"and":[119,193,197,230],"inter-signal":[120],"learning.":[121],"We":[122,173],"also":[123],"adopt":[124],"pairwise":[126],"ranking":[127],"paradigm":[128],"reduce":[130],"cascade":[132],"coupling":[133],"among":[134],"partial-order":[135],"pairs.":[136],"Furthermore,":[137],"identifying":[140],"most":[142,163],"representative":[143],"signal":[144,160,192],"batch-level":[148],"consensus":[149],"distribution":[150],"all":[152],"signals.":[153],"strategy":[155],"helps":[156],"identify":[157],"predicted":[159],"correlated":[164],"with":[165,202],"ground-truth":[171],"labels.":[172],"conduct":[174],"experiments":[175],"three":[177],"public":[178],"datasets.":[181],"The":[182],"experimental":[183],"results":[184],"demonstrate":[185],"our":[187,207],"outperforms":[189],"each":[190],"their":[194],"combinations":[195],"significantly,":[196],"can":[198,213],"even":[199],"perform":[200],"comparably":[201],"some":[203],"supervised":[204],"methods.":[205],"Additionally,":[206],"trained":[209],"one":[211],"dataset":[212],"be":[214],"transferred":[216],"other":[218,223],"datasets,":[219],"including":[220],"those":[221],"languages,":[224],"which":[225],"indicates":[226],"its":[227],"good":[228],"generalization":[229],"potential":[231],"wide":[233],"application.":[234]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
