{"id":"https://openalex.org/W3120469631","doi":"https://doi.org/10.1109/siu49456.2020.9302154","title":"Parsing Address Texts with Deep Learning Method","display_name":"Parsing Address Texts with Deep Learning Method","publication_year":2020,"publication_date":"2020-10-05","ids":{"openalex":"https://openalex.org/W3120469631","doi":"https://doi.org/10.1109/siu49456.2020.9302154","mag":"3120469631"},"language":"en","primary_location":{"id":"doi:10.1109/siu49456.2020.9302154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu49456.2020.9302154","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","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/A5022518507","display_name":"Selman Del\u0131l","orcid":"https://orcid.org/0000-0001-8149-3561"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Selman Delil","raw_affiliation_strings":["Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059288728","display_name":"Birol Kuyumcu","orcid":"https://orcid.org/0000-0002-6366-6198"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Birol Kuyumcu","raw_affiliation_strings":["Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065661615","display_name":"C\u00fcneyt Aksakall\u0131","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"C\u00fcneyt Aksakall\u0131","raw_affiliation_strings":["Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5097871091","display_name":"\u0130sa Semih Ak\u00e7\u0131ra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u0130sa Semih Ak\u00e7\u0131ra","raw_affiliation_strings":["Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sefamerve Ar-Ge Merkezi,İstanbul,Türkiye","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022518507"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.392,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.87423979,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9991999864578247,"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/T10028","display_name":"Topic Modeling","score":0.998199999332428,"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":0.9980999827384949,"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":0.8668220043182373},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7283979058265686},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7144465446472168},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.6033048033714294},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5825504064559937},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5434333682060242},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5313977003097534},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5313715934753418},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5160040855407715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4771932065486908},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.13475391268730164},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07667487859725952}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8668220043182373},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7283979058265686},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7144465446472168},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.6033048033714294},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5825504064559937},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5434333682060242},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5313977003097534},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5313715934753418},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5160040855407715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4771932065486908},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.13475391268730164},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07667487859725952}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/siu49456.2020.9302154","is_oa":false,"landing_page_url":"https://doi.org/10.1109/siu49456.2020.9302154","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8799999952316284,"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":7,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1953733484","https://openalex.org/W2143683307","https://openalex.org/W2240573979","https://openalex.org/W2534478988","https://openalex.org/W2796941002","https://openalex.org/W2910078481"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W4375867731","https://openalex.org/W579810227","https://openalex.org/W4235240664","https://openalex.org/W2952780262","https://openalex.org/W2965083567","https://openalex.org/W6643695","https://openalex.org/W4381248170","https://openalex.org/W2817971408","https://openalex.org/W3189621521"],"abstract_inverted_index":{"The":[0,135],"variability":[1],"of":[2,62,102,130],"address":[3,34,63,74,112],"writing":[4,64],"forms":[5],"makes":[6],"it":[7],"difficult":[8],"to":[9,47,52,65,71,116,132],"process":[10],"free-text":[11],"postal":[12,15],"addresses.":[13],"Since":[14],"addresses":[16],"have":[17,139],"consecutive":[18],"patterns,":[19],"sequential":[20],"probabilistic":[21],"models":[22],"such":[23],"as":[24],"CRF":[25],"or":[26],"rule-based":[27],"methods":[28,43],"are":[29,44],"often":[30],"used":[31],"in":[32,91,141],"the":[33,38,103,117,125],"parsing":[35,113],"processes.":[36],"On":[37],"other":[39],"hand,":[40],"since":[41],"these":[42],"very":[45],"sensitive":[46],"typographical":[48],"errors,":[49],"they":[50],"fail":[51],"detect":[53],"detailed":[54],"patterns":[55],"and":[56,79,109],"require":[57,151],"a":[58,99,145],"serious":[59],"expert":[60,80],"knowledge":[61,81],"develop":[66],"rules.":[67],"It":[68],"is":[69],"possible":[70],"identify":[72],"hidden":[73],"classes":[75],"without":[76],"requiring":[77],"pre-processing":[78],"by":[82],"using":[83],"deep":[84],"learning":[85],"tools":[86],"that":[87,148],"provides":[88],"successful":[89],"results":[90,118],"solving":[92],"many":[93],"similar":[94],"problems.":[95],"In":[96],"this":[97,142],"study,":[98],"one-dimensional":[100],"version":[101],"CNN":[104],"networks":[105],"has":[106,144],"been":[107],"redesigned":[108],"applied":[110],"for":[111,124],"problem.":[114],"According":[115],"obtained,":[119],"96%":[120],"accuracy":[121],"was":[122],"obtained":[123],"labeled":[126],"data":[127],"set":[128],"consisting":[129],"close":[131],"20.000":[133],"samples.":[134],"network":[136],"architecture":[137],"we":[138],"proposed":[140],"study":[143],"scalable":[146],"nature":[147],"does":[149],"not":[150],"any":[152],"pre-post":[153],"processing":[154],"stages.":[155]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}