{"id":"https://openalex.org/W1824124613","doi":"https://doi.org/10.1007/s10791-015-9273-z","title":"Topic set size design","display_name":"Topic set size design","publication_year":2015,"publication_date":"2015-10-27","ids":{"openalex":"https://openalex.org/W1824124613","doi":"https://doi.org/10.1007/s10791-015-9273-z","mag":"1824124613"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-015-9273-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-015-9273-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-015-9273-z.pdf","source":{"id":"https://openalex.org/S79460864","display_name":"Information Retrieval","issn_l":"1386-4564","issn":["1386-4564","1573-7659"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Retrieval Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10791-015-9273-z.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023595778","display_name":"Tetsuya Sakai","orcid":"https://orcid.org/0000-0002-6720-963X"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tetsuya Sakai","raw_affiliation_strings":["Department of Computer Science and Engineering, Waseda University, Tokyo, Japan","Dept. of Comput. Sci. & Eng., Waseda Univ., Tokyo, Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]},{"raw_affiliation_string":"Dept. of Comput. Sci. & Eng., Waseda Univ., Tokyo, Japan#TAB#","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5023595778"],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":{"value":1520,"currency":"USD","value_usd":1520},"apc_paid":{"value":1520,"currency":"USD","value_usd":1520},"fwci":5.1829,"has_fulltext":true,"cited_by_count":63,"citation_normalized_percentile":{"value":0.9577385,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"19","issue":"3","first_page":"256","last_page":"283"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9854999780654907,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9768000245094299,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.9606999754905701,"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/pooling","display_name":"Pooling","score":0.7551229000091553},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.7486032843589783},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.624378502368927},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.6019829511642456},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5928367376327515},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5269193053245544},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.4776098430156708},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.477353572845459},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4486461281776428},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4448249936103821},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.410757452249527},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3940759599208832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24358442425727844},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22880136966705322}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7551229000091553},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.7486032843589783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.624378502368927},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.6019829511642456},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5928367376327515},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5269193053245544},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.4776098430156708},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.477353572845459},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4486461281776428},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4448249936103821},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.410757452249527},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3940759599208832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24358442425727844},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22880136966705322},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10791-015-9273-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-015-9273-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-015-9273-z.pdf","source":{"id":"https://openalex.org/S79460864","display_name":"Information Retrieval","issn_l":"1386-4564","issn":["1386-4564","1573-7659"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Retrieval Journal","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10791-015-9273-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-015-9273-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-015-9273-z.pdf","source":{"id":"https://openalex.org/S79460864","display_name":"Information Retrieval","issn_l":"1386-4564","issn":["1386-4564","1573-7659"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Retrieval Journal","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","score":0.5,"display_name":"Decent work and economic growth"}],"awards":[{"id":"https://openalex.org/G1218873234","display_name":"Exploratory Search Considering the User's Situation","funder_award_id":"16H01756","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320322638","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W1824124613.pdf","grobid_xml":"https://content.openalex.org/works/W1824124613.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W137875067","https://openalex.org/W299870200","https://openalex.org/W578092267","https://openalex.org/W1484194788","https://openalex.org/W1507150160","https://openalex.org/W1507711745","https://openalex.org/W1553682320","https://openalex.org/W1581188666","https://openalex.org/W1948497384","https://openalex.org/W1980161923","https://openalex.org/W1989373893","https://openalex.org/W1990190154","https://openalex.org/W2017292914","https://openalex.org/W2021856948","https://openalex.org/W2023217403","https://openalex.org/W2027255954","https://openalex.org/W2035569891","https://openalex.org/W2037124948","https://openalex.org/W2041098697","https://openalex.org/W2052569738","https://openalex.org/W2058624977","https://openalex.org/W2058896506","https://openalex.org/W2059523379","https://openalex.org/W2069870183","https://openalex.org/W2075893676","https://openalex.org/W2076227143","https://openalex.org/W2078719840","https://openalex.org/W2093495945","https://openalex.org/W2096623622","https://openalex.org/W2107031757","https://openalex.org/W2132314908","https://openalex.org/W2133104961","https://openalex.org/W2135985057","https://openalex.org/W2137274315","https://openalex.org/W2144712959","https://openalex.org/W2163004772","https://openalex.org/W2186163248","https://openalex.org/W2290195878","https://openalex.org/W2294145134","https://openalex.org/W2295142358","https://openalex.org/W2339562433","https://openalex.org/W2466192277","https://openalex.org/W2515445137","https://openalex.org/W4211177544","https://openalex.org/W4241865841","https://openalex.org/W4300870773","https://openalex.org/W6603899496","https://openalex.org/W6605428294","https://openalex.org/W6633051895","https://openalex.org/W6703967046"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W147410782","https://openalex.org/W3022252430","https://openalex.org/W3103989898","https://openalex.org/W4287804464","https://openalex.org/W2238662148","https://openalex.org/W2221502901"],"abstract_inverted_index":{"Traditional":[0],"pooling-based":[1],"information":[2],"retrieval":[3],"(IR)":[4],"test":[5,48,95,216],"collections":[6,96],"typically":[7],"have":[8,169],"$$n=":[9],"50$$":[10],"\u2013100":[11],"topics,":[12],"but":[13],"it":[14],"is":[15],"difficult":[16],"for":[17,46,97,106,201],"an":[18],"IR":[19],"researcher":[20],"to":[21,40,50],"say":[22],"why":[23],"the":[24,42,73,98,102,112,121,127,131,154,182,190,193,198],"topic":[25,84,138,178,194],"set":[26,57,85,139,179,184,195],"size":[27,66,86,196],"should":[28],"really":[29],"be":[30,51],"n.":[31],"The":[32],"present":[33],"study":[34,149],"provides":[35,150],"details":[36],"on":[37,54,72],"principled":[38],"ways":[39],"determine":[41],"number":[43],"of":[44,58,100,115,120,130,162,185],"topics":[45],"a":[47,55,107,151,202],"collection":[49],"built,":[52],"based":[53,71],"specific":[56],"statistical":[59,186],"requirements.":[60],"We":[61],"employ":[62],"Nagata\u2019s":[63],"three":[64,155],"sample":[65],"design":[67,87],"techniques,":[68],"which":[69,134],"are":[70],"paired":[74],"t":[75],"test,":[76],"one-way":[77],"ANOVA,":[78],"and":[79,197],"confidence":[80],"intervals,":[81],"respectively.":[82],"These":[83],"methods":[88],"require":[89,175],"topic-by-run":[90],"score":[91],"matrices":[92],"from":[93],"past":[94],"purpose":[99],"estimating":[101],"within-system":[103,132,172],"population":[104],"variance":[105],"particular":[108,203],"evaluation":[109,166,204],"measure.":[110],"While":[111],"previous":[113],"work":[114],"Sakai":[116],"incorrectly":[117],"used":[118],"estimates":[119,129],"total":[122],"variances,":[123,133,173],"here":[124],"we":[125],"use":[126],"correct":[128],"yield":[135],"slightly":[136],"smaller":[137],"sizes":[140,180],"than":[141],"those":[142,161],"reported":[143],"previously":[144],"by":[145,188],"Sakai.":[146],"Moreover,":[147],"this":[148],"comparison":[152],"across":[153],"methods.":[156],"Our":[157],"conclusions":[158],"nevertheless":[159],"echo":[160],"Sakai:":[163],"as":[164],"different":[165,171,177],"measures":[167],"can":[168,209],"vastly":[170],"they":[174],"substantially":[176],"under":[181],"same":[183],"requirements;":[187],"analysing":[189],"tradeoff":[191],"between":[192],"pool":[199],"depth":[200],"measure":[205],"in":[206],"advance,":[207],"researchers":[208],"build":[210],"statistically":[211],"reliable":[212],"yet":[213],"highly":[214],"economical":[215],"collections.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":7},{"year":2016,"cited_by_count":6},{"year":2015,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}
