{"id":"https://openalex.org/W4399199044","doi":"https://doi.org/10.1016/j.procs.2024.04.141","title":"Hybrid Approach To Unsupervised Keyphrase Extraction","display_name":"Hybrid Approach To Unsupervised Keyphrase Extraction","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4399199044","doi":"https://doi.org/10.1016/j.procs.2024.04.141"},"language":"en","primary_location":{"id":"doi:10.1016/j.procs.2024.04.141","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2024.04.141","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1016/j.procs.2024.04.141","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041863402","display_name":"Vijender Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I4388891804","display_name":"Upgrad (India)","ror":"https://ror.org/02qw7gq21","country_code":null,"type":"education","lineage":["https://openalex.org/I4388891804"]},{"id":"https://openalex.org/I63098007","display_name":"Liverpool John Moores University","ror":"https://ror.org/04zfme737","country_code":"GB","type":"education","lineage":["https://openalex.org/I63098007"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Vijender Singh","raw_affiliation_strings":["Liverpool John Moores University, UK","UpGrad Education Private Limited, Mumbai, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Liverpool John Moores University, UK","institution_ids":["https://openalex.org/I63098007"]},{"raw_affiliation_string":"UpGrad Education Private Limited, Mumbai, India","institution_ids":["https://openalex.org/I4388891804"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004962163","display_name":"Bharath Kumar Bolla","orcid":"https://orcid.org/0000-0002-4726-042X"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bharat Kumar Bolla","raw_affiliation_strings":["Salesforce, Hyderabad, India","The University of Arizona"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Salesforce, Hyderabad, India","institution_ids":[]},{"raw_affiliation_string":"The University of Arizona","institution_ids":["https://openalex.org/I138006243"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"235","issue":null,"first_page":"1498","last_page":"1511"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9367256164550781},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.5314950346946716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5261335372924805},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35671818256378174},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.055273205041885376}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9367256164550781},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5314950346946716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5261335372924805},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35671818256378174},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.055273205041885376},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.procs.2024.04.141","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2024.04.141","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.procs.2024.04.141","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.procs.2024.04.141","pdf_url":null,"source":{"id":"https://openalex.org/S120348307","display_name":"Procedia Computer Science","issn_l":"1877-0509","issn":["1877-0509"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Procedia Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1815553412","https://openalex.org/W2064265854","https://openalex.org/W2129557600","https://openalex.org/W2167329753","https://openalex.org/W2566297247","https://openalex.org/W2604853468","https://openalex.org/W2740811004","https://openalex.org/W2790109590","https://openalex.org/W2792059528","https://openalex.org/W2890179025","https://openalex.org/W2914076857","https://openalex.org/W2949647400","https://openalex.org/W2962903510","https://openalex.org/W2963245897","https://openalex.org/W3158434946","https://openalex.org/W3159619126","https://openalex.org/W3170502199","https://openalex.org/W4280492102","https://openalex.org/W4301001724","https://openalex.org/W4390581132"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W3204019825"],"abstract_inverted_index":{"The":[0,108],"exponential":[1],"growth":[2],"of":[3,61,103,153,176,184],"textual":[4,64,217],"data":[5],"poses":[6],"a":[7,157],"monumental":[8],"challenge":[9],"for":[10,20,126,225],"extracting":[11],"meaningful":[12],"knowledge.":[13],"Manually":[14],"identifying":[15],"descriptive":[16],"keywords":[17,159],"or":[18,77,186],"keyphrases":[19,92],"each":[21],"document":[22],"is":[23,111,132],"infeasible":[24],"given":[25],"the":[26,43,58,101,135,169,193],"massive":[27],"daily":[28],"generated":[29],"text.":[30],"Automatic":[31],"keyphrase":[32,53,129,222],"extraction":[33,54],"is,":[34],"therefore,":[35],"essential.":[36],"However,":[37],"current":[38,177,203],"techniques":[39,99,178],"struggle":[40,180],"with":[41,151,229],"learning":[42],"most":[44,170],"salient":[45,161,171],"semantic":[46,172],"features":[47,183,218],"from":[48],"lengthy":[49],"documents.":[50],"This":[51],"hybrid":[52,109,195],"framework":[55],"uniquely":[56],"combines":[57],"complementary":[59,98],"strengths":[60],"graph-based":[62],"and":[63,119,216],"feature":[65],"methods.":[66,188],"Our":[67,189],"approach":[68,110,196],"demonstrates":[69],"improved":[70],"performance":[71],"over":[72],"relying":[73,104],"solely":[74],"on":[75,105,113,148,206],"statistical":[76,187],"graphical.":[78],"Graph-based":[79],"systems":[80],"leverage":[81],"word":[82],"co-":[83],"occurrence":[84],"networks":[85],"to":[86,139,181,202],"score":[87,137],"importance.":[88],"Textual":[89],"methods":[90,205],"extract":[91],"using":[93,134],"linguistic":[94],"properties.":[95],"Together,":[96],"these":[97],"overcome":[100],"limitations":[102,175],"any":[106],"strategy.":[107],"evaluated":[112],"standard":[114],"SemEval":[115,120],"2017":[116],"Task":[117,122],"10":[118],"2010":[121],"5":[123],"benchmark":[124,207],"datasets":[125],"scientific":[127],"paper":[128],"extraction.":[130],"Performance":[131],"quantified":[133],"F1":[136,199],"relative":[138],"human-annotated":[140],"ground":[141],"truth":[142],"keyphrase.":[143],"Results":[144,163],"will":[145],"quantify":[146],"effectiveness":[147],"long":[149,226],"documents":[150,227],"thousands":[152],"terms":[154],"where":[155],"only":[156],"few":[158],"represent":[160],"concepts.":[162],"show":[164],"our":[165],"technique":[166],"effectively":[167],"identifies":[168],"keywords,":[173],"overcoming":[174],"that":[179,192,212],"mix":[182],"graphical":[185],"experiments":[190],"demonstrate":[191],"proposed":[194],"achieves":[197],"superior":[198],"scores":[200],"compared":[201],"state-of-the-art":[204],"datasets.":[208],"These":[209],"results":[210],"validate":[211],"synergistically":[213],"combining":[214],"graph":[215],"enables":[219],"more":[220],"accurate":[221],"extraction,":[223],"especially":[224],"laden":[228],"extraneous":[230],"terms.":[231]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}