{"id":"https://openalex.org/W4205094352","doi":"https://doi.org/10.1145/3487664.3487712","title":"STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topics from Scientific Papers","display_name":"STEREO: A Pipeline for Extracting Experiment Statistics, Conditions, and Topics from Scientific Papers","publication_year":2021,"publication_date":"2021-11-29","ids":{"openalex":"https://openalex.org/W4205094352","doi":"https://doi.org/10.1145/3487664.3487712"},"language":"en","primary_location":{"id":"doi:10.1145/3487664.3487712","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3487664.3487712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 23rd International Conference on Information Integration and Web Intelligence","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/A5029228921","display_name":"Steffen Epp","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Steffen Epp","raw_affiliation_strings":["University Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"University Ulm, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102938971","display_name":"Marcel Hoffmann","orcid":"https://orcid.org/0000-0001-8061-9396"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marcel Hoffmann","raw_affiliation_strings":["University Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"University Ulm, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030641477","display_name":"Nicolas Lell","orcid":"https://orcid.org/0000-0002-6079-6480"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nicolas Lell","raw_affiliation_strings":["University Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"University Ulm, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024520492","display_name":"Michael M\u00f6hr","orcid":"https://orcid.org/0000-0003-0317-4367"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michael Mohr","raw_affiliation_strings":["University Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"University Ulm, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005533034","display_name":"Ansgar Scherp","orcid":"https://orcid.org/0000-0002-2653-9245"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ansgar Scherp","raw_affiliation_strings":["University Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"University Ulm, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5029228921"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57617289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"340","last_page":"349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9991000294685364,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9991000294685364,"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":0.9943000078201294,"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/T10260","display_name":"Software Engineering Research","score":0.9829000234603882,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.6665546298027039},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6487771272659302},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6209038496017456},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6001052856445312},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4969346821308136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.460791677236557},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.44800737500190735},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4312606751918793},{"id":"https://openalex.org/keywords/style","display_name":"Style (visual arts)","score":0.4191133975982666},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3994666039943695},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13963037729263306}],"concepts":[{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.6665546298027039},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6487771272659302},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6209038496017456},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6001052856445312},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4969346821308136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.460791677236557},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.44800737500190735},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4312606751918793},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.4191133975982666},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3994666039943695},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13963037729263306},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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:10.1145/3487664.3487712","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3487664.3487712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 23rd International Conference on Information Integration and Web Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8899999856948853,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W791527587","https://openalex.org/W1910875068","https://openalex.org/W2039272248","https://openalex.org/W2962741379","https://openalex.org/W2971015282","https://openalex.org/W2974767492","https://openalex.org/W2989200629","https://openalex.org/W3171712917"],"related_works":["https://openalex.org/W2992516105","https://openalex.org/W2611614995","https://openalex.org/W2368651715","https://openalex.org/W2789919619","https://openalex.org/W3210635025","https://openalex.org/W3107474891","https://openalex.org/W2020540721","https://openalex.org/W2346333693","https://openalex.org/W2777514883","https://openalex.org/W2962938354"],"abstract_inverted_index":{"A":[0],"common":[1],"writing":[2,18,146],"style":[3,202],"for":[4,184,189,204],"statistical":[5],"results":[6],"are":[7,23,30,40],"the":[8,11,38,53,107,122,177],"recommendations":[9],"of":[10,48,95,106,121,167,195],"American":[12],"Psychology":[13],"Association":[14],"(APA).":[15],"In":[16,36,140],"practice,":[17],"styles":[19,147],"vary":[20],"as":[21,161],"reports":[22,182],"not":[24,31,41,153],"100%":[25,130],"following":[26],"APA-style":[27],"or":[28],"parameters":[29],"reported":[32,42,199],"despite":[33],"being":[34],"mandatory.":[35],"addition,":[37,141],"statistics":[39,115,158,198],"in":[43,46,83,124,175,200],"isolation":[44],"but":[45],"context":[47],"experiment":[49,55,77],"conditions":[50,179],"investigated":[51],"and":[52,71,80],"general":[54],"topic.":[56],"We":[57,172],"address":[58],"these":[59],"challenges":[60],"by":[61],"proposing":[62],"a":[63,92,193],"flexible":[64],"pipeline":[65],"STEREO":[66,142],"based":[67],"on":[68,132,197],"wrapper":[69],"induction":[70],"unsupervised":[72],"aspect":[73],"detection":[74],"to":[75,85,113],"extract":[76,144,174],"statistics,":[78,134],"conditions,":[79],"topics.":[81],"Thus,":[82],"contrast":[84],"existing":[86],"rule-based":[87],"tools":[88],"like":[89],"statcheck":[90,151],"with":[91,138,148],"pre-defined":[93],"set":[94],"rules,":[96],"we":[97],"learn":[98,114],"rules":[99,117],"via":[100],"induction.":[101],"It":[102],"required":[103],"only":[104],"0.25%":[105],"CORD-19":[108],"corpus":[109],"(about":[110],"500":[111],"documents)":[112],"extraction":[116,128,191],"that":[118],"cover":[119],"95%":[120],"sentences":[123],"CORD-19.":[125],"The":[126,186],"statistic":[127],"has":[129],"precision":[131,194],"APA-conform":[133,181],"which":[135,150],"is":[136,159],"identical":[137],"statcheck.":[139],"can":[143],"non-APA":[145,156,205],"precision,":[149],"does":[152],"support.":[154],"Extracting":[155],"conform":[157],"important":[160],"they":[162],"make":[163],"more":[164],"than":[165],"99%":[166],"all":[168],"113k":[169],"extracted":[170],"statistics.":[171],"could":[173],"46%":[176],"correct":[178],"from":[180],"(30%":[183],"non-APA).":[185],"best":[187],"model":[188],"topic":[190],"achieves":[192],"75%":[196],"APA":[201],"(73%":[203],"conform).":[206]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
