{"id":"https://openalex.org/W3131157143","doi":"https://doi.org/10.1145/3397482.3450718","title":"TableLab: An Interactive Table Extraction System with Adaptive Deep Learning","display_name":"TableLab: An Interactive Table Extraction System with Adaptive Deep Learning","publication_year":2021,"publication_date":"2021-04-13","ids":{"openalex":"https://openalex.org/W3131157143","doi":"https://doi.org/10.1145/3397482.3450718","mag":"3131157143"},"language":"en","primary_location":{"id":"doi:10.1145/3397482.3450718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397482.3450718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"26th International Conference on Intelligent User Interfaces","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2102.08445","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031394103","display_name":"Nancy Xin Ru Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nancy Xin Ru Wang","raw_affiliation_strings":["Almaden Research Lab IBM Research, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Almaden Research Lab IBM Research, United States","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112041087","display_name":"Douglas Burdick","orcid":null},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Douglas Burdick","raw_affiliation_strings":["Almaden Research Lab IBM Research, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Almaden Research Lab IBM Research, United States","institution_ids":["https://openalex.org/I4210085935"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102944075","display_name":"Yunyao Li","orcid":"https://orcid.org/0009-0002-0814-4634"},"institutions":[{"id":"https://openalex.org/I4210085935","display_name":"IBM Research - Almaden","ror":"https://ror.org/005w8dd04","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210085935","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yunyao Li","raw_affiliation_strings":["Almaden Research Lab IBM Research, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Almaden Research Lab IBM Research, United States","institution_ids":["https://openalex.org/I4210085935"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210085935"],"apc_list":null,"apc_paid":null,"fwci":0.4705,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62475687,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"87","last_page":"89"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9646999835968018,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13523","display_name":"Mathematics, Computing, and Information Processing","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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":0.7573148012161255},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.6587543487548828},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5640856027603149},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.515317440032959},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4572224020957947},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39344078302383423},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1887371838092804}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7573148012161255},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6587543487548828},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5640856027603149},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.515317440032959},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4572224020957947},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39344078302383423},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1887371838092804},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3397482.3450718","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397482.3450718","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"26th International Conference on Intelligent User Interfaces","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2102.08445","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.08445","pdf_url":"https://arxiv.org/pdf/2102.08445","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2102.08445","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.08445","pdf_url":"https://arxiv.org/pdf/2102.08445","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.47999998927116394,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2786162033","https://openalex.org/W2787523828","https://openalex.org/W2919502278","https://openalex.org/W2940666954","https://openalex.org/W2990963180","https://openalex.org/W3003931580","https://openalex.org/W3022479961","https://openalex.org/W3032536818","https://openalex.org/W3034997246","https://openalex.org/W3085495757","https://openalex.org/W3107064625","https://openalex.org/W3118722740"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"Table":[0],"extraction":[1,15,97,132],"from":[2,60,130],"PDF":[3],"and":[4,44,49,88,195],"image":[5],"documents":[6],"is":[7,17],"a":[8,84,100,141,152,160,218],"ubiquitous":[9],"task":[10],"in":[11],"the":[12,29,36,46,67,105,131,192,197,200,205],"real-world.":[13],"Perfect":[14],"quality":[16],"difficult":[18,64],"to":[19,27,66,76,93,174,180,190,204,211],"achieve":[20],"with":[21,80,99,112,123,140,159,221],"one":[22],"single":[23,183],"out-of-box":[24],"model":[25,194,202,220],"due":[26,65],"(1)":[28],"wide":[30],"variety":[31,43],"of":[32,38,51,70,144,199],"table":[33,52,72,155],"styles,":[34],"(2)":[35],"lack":[37],"training":[39],"data":[40],"representing":[41],"this":[42,213],"(3)":[45],"inherent":[47],"ambiguity":[48],"subjectivity":[50],"definitions":[53],"between":[54],"end-users.":[55],"Meanwhile,":[56],"building":[57],"customized":[58,219],"models":[59,89,98],"scratch":[61],"can":[62,209],"be":[63],"expensive":[68],"nature":[69],"annotating":[71],"data.":[73],"We":[74],"attempt":[75],"solve":[77],"these":[78,175],"challenges":[79],"TableLab":[81,119,185],"by":[82,127],"providing":[83],"system":[85],"where":[86],"users":[87,171],"seamlessly":[90],"work":[91],"together":[92],"quickly":[94],"customize":[95],"high-quality":[96],"few":[101,153],"labelled":[102],"examples":[103,156],"for":[104],"user\u2019s":[106],"document":[107,117],"collection,":[108,118],"which":[109],"contains":[110],"pages":[111],"tables.":[113],"Given":[114],"an":[115,167],"input":[116],"first":[120],"detects":[121],"tables":[122,138],"similar":[124,147],"structures":[125],"(templates)":[126],"clustering":[128],"embeddings":[129],"model.":[133,165],"Document":[134],"collections":[135],"often":[136],"contain":[137],"created":[139],"limited":[142],"set":[143],"templates":[145],"or":[146],"structures.":[148],"It":[149],"then":[150,186],"selects":[151],"representative":[154],"already":[157],"extracted":[158],"pre-trained":[161,193],"base":[162],"deep":[163],"learning":[164],"Via":[166],"easy-to-use":[168],"user":[169,208],"interface,":[170],"provide":[172],"feedback":[173,189],"selections":[176],"without":[177],"necessarily":[178],"having":[179],"identify":[181],"every":[182],"error.":[184],"applies":[187],"such":[188],"finetune":[191],"returns":[196],"results":[198],"finetuned":[201],"back":[203],"user.":[206],"The":[207],"choose":[210],"repeat":[212],"process":[214],"iteratively":[215],"until":[216],"obtaining":[217],"satisfactory":[222],"performance.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2021-03-01T00:00:00"}
