{"id":"https://openalex.org/W2965762238","doi":"https://doi.org/10.1145/3292500.3330773","title":"How to Invest my Time","display_name":"How to Invest my Time","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2965762238","doi":"https://doi.org/10.1145/3292500.3330773","mag":"2965762238"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330773","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330773","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","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/A5101530173","display_name":"Shanshan Zhang","orcid":"https://orcid.org/0000-0003-2964-1082"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shanshan Zhang","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102742994","display_name":"Lihong He","orcid":null},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lihong He","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057346703","display_name":"Eduard Dragut","orcid":"https://orcid.org/0000-0002-3103-054X"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eduard Dragut","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059847153","display_name":"Slobodan Vu\u010deti\u0107","orcid":"https://orcid.org/0000-0001-5884-6293"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Slobodan Vucetic","raw_affiliation_strings":["Temple University, Philadelphia, PA, USA"],"affiliations":[{"raw_affiliation_string":"Temple University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101530173"],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":3.2203,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.93706932,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2305","last_page":"2313"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9995999932289124,"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":0.9995999932289124,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9994999766349792,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8798136711120605},{"id":"https://openalex.org/keywords/regular-expression","display_name":"Regular expression","score":0.8549355268478394},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6201705932617188},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5031582713127136},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45424091815948486},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4431047737598419},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35281801223754883},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.23859232664108276}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8798136711120605},{"id":"https://openalex.org/C121329065","wikidata":"https://www.wikidata.org/wiki/Q185612","display_name":"Regular expression","level":2,"score":0.8549355268478394},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6201705932617188},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5031582713127136},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45424091815948486},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4431047737598419},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35281801223754883},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.23859232664108276},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3292500.3330773","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330773","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W72959484","https://openalex.org/W1508480967","https://openalex.org/W1602694398","https://openalex.org/W1956471287","https://openalex.org/W1978633512","https://openalex.org/W1987538593","https://openalex.org/W2038941723","https://openalex.org/W2039532210","https://openalex.org/W2059383863","https://openalex.org/W2080666934","https://openalex.org/W2097998348","https://openalex.org/W2106950427","https://openalex.org/W2120538351","https://openalex.org/W2275294428","https://openalex.org/W2296283641","https://openalex.org/W2404161646","https://openalex.org/W2602288119","https://openalex.org/W2770860644","https://openalex.org/W2890881297","https://openalex.org/W2903158431","https://openalex.org/W4245220551"],"related_works":["https://openalex.org/W2081647779","https://openalex.org/W2789919619","https://openalex.org/W2474469336","https://openalex.org/W1590308178","https://openalex.org/W159132833","https://openalex.org/W2293417553","https://openalex.org/W3178985330","https://openalex.org/W3114114934","https://openalex.org/W2612236765","https://openalex.org/W2369950259"],"abstract_inverted_index":{"Recognizing":[0],"entities":[1,25],"that":[2,65,83,114,138],"follow":[3],"or":[4,36,90],"closely":[5],"resemble":[6],"a":[7,31,42,68,88,100,124],"regular":[8],"expression":[9],"(regex)":[10],"pattern":[11],"is":[12,48,58,126,144,152,160,184,193],"an":[13,34,52,74,78],"important":[14],"task":[15],"in":[16,41],"information":[17],"extraction.":[18],"Common":[19],"approaches":[20],"for":[21,149,165],"extraction":[22],"of":[23],"such":[24],"require":[26],"humans":[27],"to":[28,50,62,86,128,169],"either":[29,121],"write":[30,87],"regex":[32,89,131,150],"recognizing":[33],"entity":[35,39,53,93,111],"manually":[37,91],"label":[38,92],"mentions":[40],"document":[43],"corpus.":[44],"While":[45],"human":[46],"effort":[47,66],"critical":[49],"build":[51],"recognition":[54,112],"model,":[55],"surprisingly":[56],"little":[57],"known":[59],"about":[60],"how":[61],"best":[63,187],"invest":[64],"given":[67],"limited":[69],"time":[70,119,142,148,158,164],"budget.":[71],"To":[72],"get":[73],"answer,":[75],"we":[76,136],"consider":[77],"iterative":[79],"human-in-the-loop":[80],"(HIL)":[81],"framework":[82],"allows":[84],"users":[85],"mentions,":[94],"followed":[95,180],"by":[96,181],"training":[97],"and":[98,133,172,191],"refining":[99],"classifier":[101],"based":[102],"on":[103,109],"the":[104,141,157,186],"provided":[105],"information.":[106],"We":[107],"demonstrate":[108],"5":[110],"tasks":[113],"classification":[115],"accuracy":[116],"improves":[117],"over":[118],"with":[120],"approach.":[122,188],"When":[123],"user":[125],"allowed":[127],"choose":[129],"between":[130,174],"construction":[132,151],"manual":[134,166,182],"labeling,":[135],"discover":[137],"(1)":[139],"if":[140,156],"budget":[143,159],"low,":[145],"spending":[146,162],"all":[147,163],"often":[153],"advantageous,":[154],"(2)":[155],"high,":[161],"labeling":[167,183],"seems":[168],"be":[170],"superior,":[171],"(3)":[173],"those":[175],"two":[176],"extremes,":[177],"writing":[178],"regexes":[179],"typically":[185],"Our":[189],"code":[190],"data":[192],"available":[194],"at":[195],"https://github.com/nymph332088/HILRecognizer.":[196]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}