{"id":"https://openalex.org/W4284892020","doi":"https://doi.org/10.1145/3477495.3531789","title":"Recognizing Medical Search Query Intent by Few-shot Learning","display_name":"Recognizing Medical Search Query Intent by Few-shot Learning","publication_year":2022,"publication_date":"2022-07-06","ids":{"openalex":"https://openalex.org/W4284892020","doi":"https://doi.org/10.1145/3477495.3531789"},"language":"en","primary_location":{"id":"doi:10.1145/3477495.3531789","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531789","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","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":"https://openalex.org/A5051741610","display_name":"Yaqing Wang","orcid":"https://orcid.org/0000-0003-1457-1114"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaqing Wang","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100326210","display_name":"Song Wang","orcid":"https://orcid.org/0000-0002-8224-0424"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["Baidu Inc. &amp; University of Virginia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc. &amp; University of Virginia, Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324125","display_name":"Yanyan Li","orcid":"https://orcid.org/0000-0002-2258-8621"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanyan Li","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021332177","display_name":"Dejing Dou","orcid":"https://orcid.org/0000-0003-2949-6874"},"institutions":[{"id":"https://openalex.org/I98301712","display_name":"Baidu (China)","ror":"https://ror.org/03vs3wt56","country_code":"CN","type":"company","lineage":["https://openalex.org/I98301712"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dejing Dou","raw_affiliation_strings":["Baidu Inc., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Baidu Inc., Beijing, China","institution_ids":["https://openalex.org/I98301712"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I98301712"],"apc_list":null,"apc_paid":null,"fwci":3.3738,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.9384553,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"502","last_page":"512"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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/T10028","display_name":"Topic Modeling","score":0.9991999864578247,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9955000281333923,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8411604166030884},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.6260301470756531},{"id":"https://openalex.org/keywords/query-language","display_name":"Query language","score":0.6062548756599426},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.600058376789093},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5503178238868713},{"id":"https://openalex.org/keywords/web-search-query","display_name":"Web search query","score":0.48674464225769043},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.4631454646587372},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43212518095970154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.377537339925766},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.34540775418281555},{"id":"https://openalex.org/keywords/search-engine","display_name":"Search engine","score":0.30046266317367554},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11154335737228394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8411604166030884},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.6260301470756531},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.6062548756599426},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.600058376789093},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5503178238868713},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.48674464225769043},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.4631454646587372},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43212518095970154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.377537339925766},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.34540775418281555},{"id":"https://openalex.org/C97854310","wikidata":"https://www.wikidata.org/wiki/Q19541","display_name":"Search engine","level":2,"score":0.30046266317367554},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11154335737228394},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3477495.3531789","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3477495.3531789","pdf_url":null,"source":{"id":"https://openalex.org/S4363608773","display_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G6756543823","display_name":null,"funder_award_id":"2021ZD0110303","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2156037541","https://openalex.org/W2171836785","https://openalex.org/W2337562570","https://openalex.org/W2621325907","https://openalex.org/W2740110501","https://openalex.org/W2741271950","https://openalex.org/W2888338319","https://openalex.org/W2905233862","https://openalex.org/W2914971589","https://openalex.org/W2944378183","https://openalex.org/W2952866723","https://openalex.org/W2962946486","https://openalex.org/W2970183009","https://openalex.org/W2970641574","https://openalex.org/W2971296908","https://openalex.org/W2978223337","https://openalex.org/W3034445880","https://openalex.org/W3034942609","https://openalex.org/W3035282664","https://openalex.org/W3042085764","https://openalex.org/W3081232010","https://openalex.org/W3091905774","https://openalex.org/W3104390324","https://openalex.org/W3106229813","https://openalex.org/W3106241909","https://openalex.org/W3168656614","https://openalex.org/W3176625957","https://openalex.org/W3188976178","https://openalex.org/W3199484478","https://openalex.org/W3209002790","https://openalex.org/W4256361765","https://openalex.org/W4292779060","https://openalex.org/W6778883912"],"related_works":["https://openalex.org/W2572349046","https://openalex.org/W3197639690","https://openalex.org/W2096359267","https://openalex.org/W1981131819","https://openalex.org/W2026738364","https://openalex.org/W2017989738","https://openalex.org/W2124814993","https://openalex.org/W5304494","https://openalex.org/W2146885082","https://openalex.org/W4236234562"],"abstract_inverted_index":{"Online":[0],"healthcare":[1],"services":[2],"can":[3],"provide":[4],"unlimited":[5],"and":[6,16,42,48,153],"in-time":[7],"medical":[8,29,56,59,89,137,172],"information":[9],"to":[10,53,66,107,124],"users,":[11],"which":[12,122,142,156],"promotes":[13],"social":[14],"goods":[15],"breaks":[17],"the":[18,24,28,55,110,144,151,179],"barriers":[19],"of":[20,112,130,147,181],"locations.":[21],"However,":[22],"understanding":[23],"user":[25,101],"intents":[26,60,71],"behind":[27],"related":[30],"queries":[31,38,99,126],"is":[32,157],"a":[33,74,84,118,128,170],"challenging":[34],"problem.":[35],"Medical":[36],"search":[37,90,102,173],"are":[39,61],"usually":[40],"short":[41],"noisy,":[43],"lack":[44,111],"strict":[45],"syntactic":[46,140],"structure,":[47],"also":[49,116],"require":[50],"professional":[51],"background":[52],"understand":[54],"terms.":[57],"The":[58],"fine-grained,":[62],"making":[63],"them":[64],"hard":[65],"recognize.":[67],"In":[68],"addition,":[69],"many":[70],"only":[72],"have":[73],"few":[75],"labeled":[76,113],"data.":[77,114],"To":[78],"handle":[79],"these":[80],"problems,":[81],"we":[82],"propose":[83],"few-shot":[85],"learning":[86],"method":[87],"for":[88,109],"query":[91,120,174],"intent":[92,175],"recognition":[93,176],"called":[94],"MEDIC.":[95,182],"We":[96,115],"extract":[97],"co-click":[98],"from":[100,163],"logs":[103],"as":[104,127],"weak":[105],"supervision":[106],"compensate":[108],"design":[117],"new":[119],"encoder":[121],"learns":[123],"represent":[125],"combination":[129],"semantic":[131],"knowledge":[132,138,141,155],"recorded":[133],"in":[134,150],"an":[135],"external":[136],"graph,":[139],"marks":[143],"grammatical":[145],"role":[146],"each":[148],"word":[149],"query,":[152],"generic":[154],"captured":[158],"by":[159],"language":[160],"models":[161],"pretrained":[162],"large-scale":[164],"text":[165],"corpus.":[166],"Experimental":[167],"results":[168],"on":[169],"real":[171],"dataset":[177],"validate":[178],"effectiveness":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
