{"id":"https://openalex.org/W6941165659","doi":"https://doi.org/10.1109/ccwc62904.2025.10903719","title":"Multi-Stage Retriever Model for Document Classification Using Fine Tuned Embedding Model","display_name":"Multi-Stage Retriever Model for Document Classification Using Fine Tuned Embedding Model","publication_year":2025,"publication_date":"2025-01-06","ids":{"openalex":"https://openalex.org/W6941165659","doi":"https://doi.org/10.1109/ccwc62904.2025.10903719"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc62904.2025.10903719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc62904.2025.10903719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"},"type":"conference-paper","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":null,"display_name":"Navaneeth Amarnath","orcid":null},"institutions":[{"id":"https://openalex.org/I110675161","display_name":"Infosys (India)","ror":"https://ror.org/03bs18y54","country_code":"IN","type":"company","lineage":["https://openalex.org/I110675161"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Navaneeth Amarnath","raw_affiliation_strings":["Infrrd.ai,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Infrrd.ai,Bengaluru,India","institution_ids":["https://openalex.org/I110675161"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Saiteja Tallam","orcid":null},"institutions":[{"id":"https://openalex.org/I110675161","display_name":"Infosys (India)","ror":"https://ror.org/03bs18y54","country_code":"IN","type":"company","lineage":["https://openalex.org/I110675161"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Saiteja Tallam","raw_affiliation_strings":["Infrrd.ai,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Infrrd.ai,Bengaluru,India","institution_ids":["https://openalex.org/I110675161"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Pratyusha Rasamsetty","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162346","display_name":"Hospital Universitario Infantil San Jos\u00e9","ror":"https://ror.org/05at6sw30","country_code":"CO","type":"healthcare","lineage":["https://openalex.org/I4210162346"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Pratyusha Rasamsetty","raw_affiliation_strings":["Infrrd.ai,San Jose,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Infrrd.ai,San Jose,USA","institution_ids":["https://openalex.org/I4210162346"]}]},{"author_position":"last","author":{"id":null,"display_name":"Deepak Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I110675161","display_name":"Infosys (India)","ror":"https://ror.org/03bs18y54","country_code":"IN","type":"company","lineage":["https://openalex.org/I110675161"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Deepak Kumar","raw_affiliation_strings":["Infrrd.ai,Bengaluru,India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Infrrd.ai,Bengaluru,India","institution_ids":["https://openalex.org/I110675161"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"00209","last_page":"00218"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10451","display_name":"Mycorrhizal Fungi and Plant Interactions","score":0.54830002784729,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10451","display_name":"Mycorrhizal Fungi and Plant Interactions","score":0.54830002784729,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.13099999725818634,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10825","display_name":"Plant Pathogens and Fungal Diseases","score":0.042899999767541885,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6786999702453613},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6717000007629395},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6410999894142151},{"id":"https://openalex.org/keywords/document-classification","display_name":"Document classification","score":0.5623000264167786},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3919000029563904},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.34869998693466187},{"id":"https://openalex.org/keywords/document-image-processing","display_name":"Document image processing","score":0.33410000801086426},{"id":"https://openalex.org/keywords/document-clustering","display_name":"Document clustering","score":0.3167000114917755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7419000267982483},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6786999702453613},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6717000007629395},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6410999894142151},{"id":"https://openalex.org/C2780479914","wikidata":"https://www.wikidata.org/wiki/Q302088","display_name":"Document classification","level":2,"score":0.5623000264167786},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4945000112056732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48080000281333923},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44690001010894775},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C2988504005","wikidata":"https://www.wikidata.org/wiki/Q379942","display_name":"Document image processing","level":4,"score":0.33410000801086426},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.3167000114917755},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C67905146","wikidata":"https://www.wikidata.org/wiki/Q5287646","display_name":"Document processing","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.29760000109672546},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C2278759","wikidata":"https://www.wikidata.org/wiki/Q831677","display_name":"Document management system","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27720001339912415},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.263700008392334},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2606000006198883}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccwc62904.2025.10903719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc62904.2025.10903719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2605976347","https://openalex.org/W2911489562","https://openalex.org/W2962739339","https://openalex.org/W2997154779","https://openalex.org/W3013022628","https://openalex.org/W4385571843","https://openalex.org/W4402422279","https://openalex.org/W6636510571","https://openalex.org/W6679957877","https://openalex.org/W6755207826","https://openalex.org/W6777399232","https://openalex.org/W6784397084"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,14,27,34,43,59,81,91,111,136],"efficient":[2],"classification":[3,20,87],"of":[4,36,73],"business":[5,37,140],"documents":[6,38],"is":[7],"essential":[8],"for":[9,98,139],"organizations":[10],"to":[11,63,76,129],"streamline":[12],"operations":[13],"enhance":[15],"data":[16],"management.":[17],"Traditional":[18],"document":[19,61,115,141],"methods,":[21,131],"relying":[22],"on":[23],"manual":[24],"feature":[25],"extraction":[26],"annotation,":[28],"often":[29],"struggle":[30],"with":[31],"the":[32,70,78,108],"diversity":[33],"complexity":[35],"such":[39],"as":[40],"invoices,":[41],"contracts,":[42],"financial":[44],"reports.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49,106],"propose":[50],"a":[51,102,133],"novel":[52],"approach":[53,124],"that":[54,122],"utilizes":[55],"custom":[56],"vector":[57,74],"embeddings":[58,75],"predefined":[60],"classes":[62],"address":[64],"these":[65],"challenges.":[66],"Our":[67],"method":[68],"leverages":[69],"semantic":[71],"richness":[72],"capture":[77],"inherent":[79],"meanings":[80],"relationships":[82],"within":[83],"documents,":[84],"enabling":[85],"precise":[86],"even":[88],"in":[89],"complex":[90],"nuanced":[92],"contexts.":[93],"By":[94],"fine-tuning":[95],"embedding":[96],"models":[97],"retrieval":[99],"tasks":[100],"using":[101],"proprietary":[103],"in-house":[104],"dataset,":[105],"ensure":[107],"model's":[109],"robustness":[110],"scalability":[112],"across":[113],"various":[114],"types.":[116],"We":[117],"demonstrate":[118],"through":[119],"extensive":[120],"experiments":[121],"our":[123],"achieves":[125],"superior":[126],"performance":[127],"compared":[128],"traditional":[130],"offering":[132],"scalable,":[134],"accurate,":[135],"interpretable":[137],"solution":[138],"classification.":[142]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}