{"id":"https://openalex.org/W6998699888","doi":null,"title":"Automated extraction of polyphenols contents in food from the scientific literature using deep learning techniques","display_name":"Automated extraction of polyphenols contents in food from the scientific literature using deep learning techniques","publication_year":2024,"publication_date":"2024-07-01","ids":{"openalex":"https://openalex.org/W6998699888"},"language":"en","primary_location":{"id":"pmh:oai:oa.upm.es:82933","doi":null,"is_oa":true,"landing_page_url":"https://oa.upm.es/82933/","pdf_url":"https://oa.upm.es/82933/1/TFM_IRUNE_MONREAL_IRACEBURU.pdf","source":{"id":"https://openalex.org/S4377196323","display_name":"UPM Digital Archive (Technical University of Madrid)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I88060688","host_organization_name":"Universidad Polit\u00e9cnica de Madrid","host_organization_lineage":["https://openalex.org/I88060688"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/masterThesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://oa.upm.es/82933/1/TFM_IRUNE_MONREAL_IRACEBURU.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Monreal Iraceburu, Irune","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Monreal Iraceburu, Irune","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.7318999767303467,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.7318999767303467,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.23749999701976776,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.008299999870359898,"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/polyphenols","display_name":"polyphenols","score":0.9150000214576721},{"id":"https://openalex.org/keywords/named-entity-recognition","display_name":"named entity recognition","score":0.9509999752044678},{"id":"https://openalex.org/keywords/biobert","display_name":"BioBERT","score":0.9580000042915344},{"id":"https://openalex.org/keywords/nutritional-database","display_name":"nutritional database","score":0.8240000009536743},{"id":"https://openalex.org/keywords/adamw-optimizer","display_name":"AdamW optimizer","score":0.7720000147819519},{"id":"https://openalex.org/keywords/data-augmentation","display_name":"data augmentation","score":0.8259999752044678},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.7239999771118164},{"id":"https://openalex.org/keywords/biomedical-text-mining","display_name":"biomedical text mining","score":0.5699999928474426},{"id":"https://openalex.org/keywords/relation-extraction","display_name":"relation extraction","score":0.5370000004768372},{"id":"https://openalex.org/keywords/food-composition-data","display_name":"food composition data","score":0.38999998569488525},{"id":"https://openalex.org/keywords/food-traceability","display_name":"food traceability","score":0.3160000145435333}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6510999798774719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.642300009727478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6258999705314636},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6018000245094299},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5999000072479248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5497999787330627},{"id":"https://openalex.org/C70899900","wikidata":"https://www.wikidata.org/wiki/Q290439","display_name":"Polyphenol","level":3,"score":0.44179999828338623},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.42289999127388},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.4120999872684479},{"id":"https://openalex.org/C2777466982","wikidata":"https://www.wikidata.org/wiki/Q5227287","display_name":"Data extraction","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C2781404680","wikidata":"https://www.wikidata.org/wiki/Q1806341","display_name":"Novel food","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2776091240","wikidata":"https://www.wikidata.org/wiki/Q106238460","display_name":"Plant identification","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:oa.upm.es:82933","doi":null,"is_oa":true,"landing_page_url":"https://oa.upm.es/82933/","pdf_url":"https://oa.upm.es/82933/1/TFM_IRUNE_MONREAL_IRACEBURU.pdf","source":{"id":"https://openalex.org/S4377196323","display_name":"UPM Digital Archive (Technical University of Madrid)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I88060688","host_organization_name":"Universidad Polit\u00e9cnica de Madrid","host_organization_lineage":["https://openalex.org/I88060688"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/masterThesis"},{"id":"pmh:oai:hispana.mcu.es:61131889","doi":null,"is_oa":true,"landing_page_url":"https://oa.upm.es/82933/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196108","display_name":"Hispana","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/masterThesis"}],"best_oa_location":{"id":"pmh:oai:oa.upm.es:82933","doi":null,"is_oa":true,"landing_page_url":"https://oa.upm.es/82933/","pdf_url":"https://oa.upm.es/82933/1/TFM_IRUNE_MONREAL_IRACEBURU.pdf","source":{"id":"https://openalex.org/S4377196323","display_name":"UPM Digital Archive (Technical University of Madrid)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I88060688","host_organization_name":"Universidad Polit\u00e9cnica de Madrid","host_organization_lineage":["https://openalex.org/I88060688"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/masterThesis"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6998699888.pdf","grobid_xml":"https://content.openalex.org/works/W6998699888.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1520377376","https://openalex.org/W1815876077","https://openalex.org/W1940872118","https://openalex.org/W2008056655","https://openalex.org/W2050331639","https://openalex.org/W2105594594","https://openalex.org/W2123556395","https://openalex.org/W2129999749","https://openalex.org/W2141869602","https://openalex.org/W2273396394","https://openalex.org/W2626778328","https://openalex.org/W2758765981","https://openalex.org/W2785787385","https://openalex.org/W2787560479","https://openalex.org/W2885195348","https://openalex.org/W2896457183","https://openalex.org/W2911489562","https://openalex.org/W2925863688","https://openalex.org/W2962902328","https://openalex.org/W2963406669","https://openalex.org/W3030163527","https://openalex.org/W3041133507","https://openalex.org/W3081420850","https://openalex.org/W3184579859","https://openalex.org/W4322718191","https://openalex.org/W4391124783","https://openalex.org/W4391292768"],"related_works":["https://openalex.org/W4318147636","https://openalex.org/W7030509624","https://openalex.org/W3204805844","https://openalex.org/W4302333993","https://openalex.org/W3187709454","https://openalex.org/W3048338659","https://openalex.org/W2950659199","https://openalex.org/W3206618979","https://openalex.org/W4417508210","https://openalex.org/W3036723235"],"abstract_inverted_index":{"This":[0],"thesis":[1,336],"explores":[2],"the":[3,12,31,50,68,73,89,155,158,166,171,191,225,234,245,275,280,283,309,313,323,368,389,413,429,435],"application":[4,223,437],"of":[5,14,25,33,39,53,77,91,105,157,173,219,224,266,277,286,303,315,325,361,371,377,431,438],"BioBERT,":[6],"a":[7,102,129,304,346],"specialized":[8],"BERT":[9],"model,":[10,193],"for":[11,121,308,349,356],"identification":[13],"polyphenol":[15,34,110,140,201,278,326,404],"and":[16,63,75,87,114,123,135,141,183,202,211,215,257,299,317,330,380,400,402,419,434,442],"food":[17,142,203],"entities":[18,204],"in":[19,112,237,259,289,352,373,406,440],"scientific":[20,40,56,106,290,365,424,444],"literature.":[21],"The":[22,98,187,221,264,301,359],"primary":[23],"objective":[24],"this":[26,116,126,267,335],"dissertation":[27],"is":[28],"to":[29,71,84,109,118,137,180,248,251,395,428],"automate":[30],"extraction":[32,79,276,393,422],"content":[35,111],"from":[36,423],"vast":[37],"collections":[38],"texts":[41],"using":[42],"Named":[43],"Entity":[44],"Recognition":[45],"(NER)":[46],"techniques,":[47,198],"thereby":[48,176,321],"addressing":[49],"significant":[51,149],"challenge":[52,285],"managing":[54,441],"extensive":[55],"data.":[57,445],"By":[58,273],"leveraging":[59,353],"state-of-the-art":[60],"deep":[61,130],"learning":[62,131],"natural":[64],"language":[65],"processing":[66],"methodologies,":[67],"study":[69,281],"aims":[70],"enhance":[72,412],"accuracy":[74,218],"efficiency":[76],"data":[78,117,287],"processes,":[80],"contributing":[81,427],"valuable":[82],"insights":[83,295],"nutritional":[85,93,319,357,378,432],"research":[86,268,327,385,433],"supporting":[88,381],"maintenance":[90,314],"comprehensive":[92],"databases":[94],"such":[95],"as":[96,231],"Phenol-Explorer.":[97],"methodology":[99],"involved":[100],"collecting":[101],"diverse":[103],"dataset":[104],"papers":[107],"related":[108],"foods":[113],"preprocessing":[115],"ensure":[119],"suitability":[120],"training":[122,174],"evaluation.":[124],"For":[125],"very":[127],"purpose,":[128],"model":[132],"was":[133,152],"designed":[134],"trained":[136],"accurately":[138],"extract":[139],"entities.":[143],"Initial":[144],"experiments":[145],"without":[146],"optimization":[147],"revealed":[148],"overfitting,":[150,230],"which":[151],"mitigated":[153],"by":[154,169,233],"implementation":[156],"AdamW":[159,226],"optimizer.":[160],"Data":[161],"augmentation":[162],"techniques":[163],"further":[164,411],"enhanced":[165],"model's":[167,246,414],"robustness":[168,256],"increasing":[170],"diversity":[172],"samples,":[175],"improving":[177],"its":[178,255,340],"resilience":[179],"noisy":[181],"inputs":[182],"minor":[184],"textual":[185],"variations.":[186],"results":[188],"demonstrate":[189],"that":[190,293],"BioBERT":[192,362],"combined":[194],"with":[195,205],"advanced":[196],"NER":[197],"effectively":[199],"detects":[200],"high":[206],"accuracy,":[207],"achieving":[208],"precision,":[209],"recall":[210],"F1-scores":[212],"around":[213],"0.98,":[214],"an":[216],"overall":[217],"0.99.":[220],"successful":[222],"optimizer":[227],"significantly":[228],"reduced":[229],"evidenced":[232],"consistent":[235],"decrease":[236],"validation":[238],"loss":[239],"across":[240],"trials.":[241],"These":[242,408],"outcomes":[243],"validate":[244],"capability":[247],"generalize":[249],"well":[250],"unseen":[252],"data,":[253,279,320],"underscoring":[254],"adaptability":[258],"handling":[260],"complex":[261],"biomedical":[262],"information.":[263],"contributions":[265],"extend":[269],"beyond":[270],"technical":[271],"achievements.":[272],"automating":[274],"addresses":[282],"ongoing":[284],"overload":[288],"research,":[291],"ensuring":[292],"critical":[294],"are":[296],"efficiently":[297],"extracted":[298],"used.":[300],"establishment":[302],"continuous":[305],"update":[306],"mechanism":[307],"Phenol-Explorer":[310],"database":[311],"supports":[312],"accurate":[316],"relevant":[318],"enhancing":[322],"impact":[324],"on":[328],"health":[329],"nutrition":[331],"science.":[332],"In":[333],"conclusion,":[334],"not":[337],"only":[338],"achieves":[339],"initial":[341],"objectives":[342],"but,":[343],"also":[344],"lays":[345],"solid":[347],"foundation":[348],"future":[350],"advancements":[351,409],"artificial":[354],"intelligence":[355],"research.":[358],"integration":[360],"through":[363],"rigorous":[364],"methodologies":[366],"exemplifies":[367],"transformative":[369],"potential":[370],"AI":[372,439],"advancing":[374],"our":[375],"understanding":[376],"sciences":[379],"evidence-based":[382],"practices.":[383],"Future":[384],"directions":[386],"include":[387],"expanding":[388],"dataset,":[390],"incorporating":[391],"relation":[392],"methods":[394],"identify":[396],"interactions":[397],"between":[398],"polyphenols":[399],"foods,":[401],"quantifying":[403],"concentrations":[405],"foods.":[407],"will":[410],"capabilities,":[415],"enabling":[416],"more":[417],"detailed":[418],"automated":[420],"information":[421],"papers,":[425],"ultimately":[426],"advancement":[430],"broader":[436],"analysing":[443]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}