{"id":"https://openalex.org/W2159849140","doi":"https://doi.org/10.18653/v1/s15-2001","title":"SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)","display_name":"SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2159849140","doi":"https://doi.org/10.18653/v1/s15-2001","mag":"2159849140"},"language":"en","primary_location":{"id":"doi:10.18653/v1/s15-2001","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s15-2001","pdf_url":"https://www.aclweb.org/anthology/S15-2001.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/S15-2001.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070905937","display_name":"Wei Xu","orcid":"https://orcid.org/0000-0002-7044-3232"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Xu","raw_affiliation_strings":["University of Pennsylvania Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068508539","display_name":"Chris Callison-Burch","orcid":"https://orcid.org/0000-0001-8196-1943"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chris Callison-Burch","raw_affiliation_strings":["University of Pennsylvania Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057693146","display_name":"Bill Dolan","orcid":null},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bill Dolan","raw_affiliation_strings":["Microsoft Research Redmond, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Redmond, WA, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":148,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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/T10028","display_name":"Topic Modeling","score":1.0,"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":0.9984999895095825,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9977999925613403,"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/paraphrase","display_name":"Paraphrase","score":0.8138009309768677},{"id":"https://openalex.org/keywords/semeval","display_name":"SemEval","score":0.7554481625556946},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7286428809165955},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6931113600730896},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6600337028503418},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.6169229745864868},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.560575008392334},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.5575893521308899},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.555296778678894},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4803406000137329},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4738638401031494},{"id":"https://openalex.org/keywords/lexical-diversity","display_name":"Lexical diversity","score":0.47056910395622253},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.45587509870529175},{"id":"https://openalex.org/keywords/scripting-language","display_name":"Scripting language","score":0.4231048822402954},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18225520849227905},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.16791504621505737},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.134549081325531},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13237184286117554},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.10142678022384644}],"concepts":[{"id":"https://openalex.org/C2780922921","wikidata":"https://www.wikidata.org/wiki/Q255189","display_name":"Paraphrase","level":2,"score":0.8138009309768677},{"id":"https://openalex.org/C44572571","wikidata":"https://www.wikidata.org/wiki/Q7448970","display_name":"SemEval","level":3,"score":0.7554481625556946},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7286428809165955},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6931113600730896},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6600337028503418},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.6169229745864868},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.560575008392334},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.5575893521308899},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.555296778678894},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4803406000137329},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4738638401031494},{"id":"https://openalex.org/C2781202465","wikidata":"https://www.wikidata.org/wiki/Q18346297","display_name":"Lexical diversity","level":3,"score":0.47056910395622253},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.45587509870529175},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.4231048822402954},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18225520849227905},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.16791504621505737},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.134549081325531},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13237184286117554},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.10142678022384644},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/s15-2001","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s15-2001","pdf_url":"https://www.aclweb.org/anthology/S15-2001.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.710.7076","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.710.7076","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cis.upenn.edu/%7Exwe/publications/semeval_pit_2015_overview.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.723.874","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.723.874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cis.upenn.edu/%7Eccb/publications/paraphrase-and-semantic-similarity-in-twitter.pdf","raw_type":"text"}],"best_oa_location":{"id":"doi:10.18653/v1/s15-2001","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/s15-2001","pdf_url":"https://www.aclweb.org/anthology/S15-2001.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4399999976158142}],"awards":[{"id":"https://openalex.org/G4233727642","display_name":"EAGER: Simplification as Machine Translation","funder_award_id":"1430651","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2159849140.pdf","grobid_xml":"https://content.openalex.org/works/W2159849140.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W1788602","https://openalex.org/W97201605","https://openalex.org/W100656083","https://openalex.org/W138005451","https://openalex.org/W1493309689","https://openalex.org/W1498924130","https://openalex.org/W1566018662","https://openalex.org/W1901600440","https://openalex.org/W1980776243","https://openalex.org/W1989420837","https://openalex.org/W1998498767","https://openalex.org/W2051593977","https://openalex.org/W2060786818","https://openalex.org/W2088911157","https://openalex.org/W2093219119","https://openalex.org/W2099884836","https://openalex.org/W2103305545","https://openalex.org/W2104671598","https://openalex.org/W2105846817","https://openalex.org/W2110503409","https://openalex.org/W2112836372","https://openalex.org/W2116900724","https://openalex.org/W2118296893","https://openalex.org/W2126649078","https://openalex.org/W2135875128","https://openalex.org/W2138738738","https://openalex.org/W2138934023","https://openalex.org/W2141766660","https://openalex.org/W2146357154","https://openalex.org/W2153617695","https://openalex.org/W2153702313","https://openalex.org/W2153848201","https://openalex.org/W2155484602","https://openalex.org/W2157835502","https://openalex.org/W2160177523","https://openalex.org/W2164290393","https://openalex.org/W2167109146","https://openalex.org/W2243781846","https://openalex.org/W2250293945","https://openalex.org/W2250305120","https://openalex.org/W2250424588","https://openalex.org/W2250565861","https://openalex.org/W2251291469","https://openalex.org/W2251861449","https://openalex.org/W2478941700","https://openalex.org/W4206315171","https://openalex.org/W4237910115","https://openalex.org/W6604150846","https://openalex.org/W6629886455","https://openalex.org/W6649871043","https://openalex.org/W6680239946","https://openalex.org/W6683542830","https://openalex.org/W6691695795","https://openalex.org/W6721511812","https://openalex.org/W6805520290","https://openalex.org/W6903629498"],"related_works":["https://openalex.org/W2216851945","https://openalex.org/W3118824058","https://openalex.org/W3048733152","https://openalex.org/W4287690375","https://openalex.org/W2752041471","https://openalex.org/W78638240","https://openalex.org/W2252122760","https://openalex.org/W2105461184","https://openalex.org/W3197133407","https://openalex.org/W2159849140"],"abstract_inverted_index":{"In":[0],"this":[1],"shared":[2,144],"task,":[3],"we":[4],"present":[5],"evaluations":[6],"on":[7,140],"two":[8,165],"related":[9],"tasks":[10,157],"Paraphrase":[11,54],"Identification":[12],"(PI)":[13],"and":[14,74,86,104,127,155,158,176],"Semantic":[15],"Textual":[16],"Similarity":[17],"(SS)":[18],"systems":[19,94,134,175],"for":[20,89,100,110],"the":[21,71,78,101,111,131,150,153,160,172],"Twitter":[22,53],"data.":[23],"Given":[24],"a":[25,34,39,50,96,105,117],"pair":[26],"of":[27,63,98,108,124],"sentences,":[28],"participants":[29],"are":[30],"asked":[31],"to":[32,42,70,77,116,162],"produce":[33],"binary":[35],"yes/no":[36],"judgement":[37],"or":[38],"graded":[40],"score":[41],"measure":[43],"their":[44],"semantic":[45],"equivalence.":[46],"The":[47,81,92],"task":[48,73,103,113,145],"features":[49],"newly":[51],"constructed":[52],"Corpus":[55],"that":[56],"contains":[57],"18,762":[58],"sentence":[59],"pairs.":[60],"A":[61],"total":[62],"19":[64],"teams":[65],"participated,":[66],"submitting":[67],"36":[68],"runs":[69,76],"PI":[72,102,154],"26":[75],"SS":[79,112,133,156],"task.":[80],"evaluation":[82,177],"shows":[83],"encouraging":[84],"results":[85],"open":[87],"challenges":[88],"future":[90],"research.":[91],"best":[93,132],"scored":[95],"F1-measure":[97],"0.674":[99],"Pearson":[106,139],"correlation":[107],"0.619":[109],"respectively,":[114],"comparing":[115],"strong":[118],"baseline":[119,174],"using":[120],"logistic":[121],"regression":[122],"model":[123],"0.589":[125],"F1":[126],"0.511":[128],"Pearson;":[129],"while":[130],"can":[135],"often":[136],"reach":[137],">0.80":[138],"well-formed":[141],"text.":[142],"This":[143],"also":[146],"provides":[147],"insights":[148],"into":[149],"relation":[151],"between":[152],"suggests":[159],"importance":[161],"bringing":[163],"these":[164],"research":[166],"areas":[167],"together.":[168],"We":[169],"make":[170],"all":[171],"data,":[173],"scripts":[178],"publicly":[179],"available.":[180],"1":[181]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":13},{"year":2020,"cited_by_count":13},{"year":2019,"cited_by_count":13},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":22},{"year":2016,"cited_by_count":23},{"year":2015,"cited_by_count":16},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}