{"id":"https://openalex.org/W2970232715","doi":"https://doi.org/10.18653/v1/w19-5027","title":"ChiMed: A Chinese Medical Corpus for Question Answering","display_name":"ChiMed: A Chinese Medical Corpus for Question Answering","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2970232715","doi":"https://doi.org/10.18653/v1/w19-5027","mag":"2970232715"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w19-5027","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5027","pdf_url":"https://www.aclweb.org/anthology/W19-5027.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th BioNLP Workshop and Shared Task","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W19-5027.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024080570","display_name":"Yuanhe Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuanhe Tian","raw_affiliation_strings":["Department of Linguistics University of Washington"],"affiliations":[{"raw_affiliation_string":"Department of Linguistics University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043402333","display_name":"Weicheng Ma","orcid":"https://orcid.org/0000-0001-7494-9874"},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weicheng Ma","raw_affiliation_strings":["Computer Science Department New York University"],"affiliations":[{"raw_affiliation_string":"Computer Science Department New York University","institution_ids":["https://openalex.org/I57206974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100676786","display_name":"Fei Xia","orcid":"https://orcid.org/0009-0002-4609-9950"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Xia","raw_affiliation_strings":["Department of Linguistics University of Washington"],"affiliations":[{"raw_affiliation_string":"Department of Linguistics University of Washington","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100381754","display_name":"Yan Song","orcid":"https://orcid.org/0000-0002-2849-2962"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Song","raw_affiliation_strings":["Tencent AI Lab"],"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5024080570"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":2.4749,"has_fulltext":true,"cited_by_count":31,"citation_normalized_percentile":{"value":0.9178837,"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":"250","last_page":"260"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":1.0,"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":1.0,"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":0.9994000196456909,"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":0.991599977016449,"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":0.8523221015930176},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.781764030456543},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7772435545921326},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.7557778358459473},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.7172210216522217},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6979061365127563},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6334137916564941},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4551711678504944},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.42571526765823364},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4221356511116028},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4162364602088928}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8523221015930176},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.781764030456543},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7772435545921326},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.7557778358459473},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.7172210216522217},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6979061365127563},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6334137916564941},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4551711678504944},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42571526765823364},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4221356511116028},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4162364602088928},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w19-5027","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5027","pdf_url":"https://www.aclweb.org/anthology/W19-5027.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th BioNLP Workshop and Shared Task","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w19-5027","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w19-5027","pdf_url":"https://www.aclweb.org/anthology/W19-5027.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th BioNLP Workshop and Shared Task","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8500000238418579,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970232715.pdf","grobid_xml":"https://content.openalex.org/works/W2970232715.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1544827683","https://openalex.org/W2103479483","https://openalex.org/W2120735855","https://openalex.org/W2170738476","https://openalex.org/W2250522181","https://openalex.org/W2251584595","https://openalex.org/W2251655167","https://openalex.org/W2251818205","https://openalex.org/W2252217313","https://openalex.org/W2296024507","https://openalex.org/W2536015822","https://openalex.org/W2538374209","https://openalex.org/W2539671052","https://openalex.org/W2609826708","https://openalex.org/W2742049800","https://openalex.org/W2769934148","https://openalex.org/W2804552794","https://openalex.org/W2805053844","https://openalex.org/W2872710616","https://openalex.org/W2885644005","https://openalex.org/W2891113091","https://openalex.org/W2949615363","https://openalex.org/W2950193743","https://openalex.org/W2951359136","https://openalex.org/W2963506049","https://openalex.org/W2963626623","https://openalex.org/W2963748441","https://openalex.org/W2963957489","https://openalex.org/W2964061924","https://openalex.org/W2964223283","https://openalex.org/W4299606006"],"related_works":["https://openalex.org/W2384605597","https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W2387743295","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W4287241967","https://openalex.org/W3144173820"],"abstract_inverted_index":{"Question":[0],"answering":[1],"(QA)":[2],"is":[3,15],"a":[4,31,48,71,82],"challenging":[5],"task":[6,110],"in":[7,23],"natural":[8],"language":[9],"processing":[10],"(NLP),":[11],"especially":[12,60],"when":[13],"it":[14],"applied":[16,120],"to":[17,30,41,88,121],"specific":[18],"domains.":[19],"While":[20],"models":[21,118],"trained":[22],"the":[24,62,86,90,93,101,107,112,122],"general":[25],"domain":[26,42],"can":[27,46],"be":[28],"adapted":[29],"new":[32],"target":[33],"domain,":[34],"their":[35],"performance":[36],"often":[37],"degrades":[38],"significantly":[39],"due":[40],"mismatch.":[43],"Alternatively,":[44],"one":[45],"require":[47],"large":[49],"amount":[50],"of":[51,85,92],"domain-specific":[52],"QA":[53,75],"data,":[54],"but":[55],"such":[56],"data":[57],"are":[58,119],"rare,":[59],"for":[61,106,127],"medical":[63,74],"domain.":[64],"In":[65],"this":[66],"study,":[67],"we":[68,80,96],"first":[69],"collect":[70],"large-scale":[72],"Chinese":[73],"corpus":[76,87,102],"called":[77],"ChiMed;":[78],"second":[79],"annotate":[81],"small":[83],"fraction":[84],"check":[89],"quality":[91],"answers;":[94],"third,":[95],"extract":[97],"two":[98],"datasets":[99],"from":[100],"and":[103,111],"use":[104],"them":[105],"relevancy":[108],"prediction":[109,114],"adoption":[113],"task.":[115],"Several":[116],"benchmark":[117],"datasets,":[123],"producing":[124],"good":[125],"results":[126],"both":[128],"tasks.":[129]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":5}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}
