{"id":"https://openalex.org/W2984684268","doi":"https://doi.org/10.18653/v1/d19-6204","title":"Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction","display_name":"Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2984684268","doi":"https://doi.org/10.18653/v1/d19-6204","mag":"2984684268"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d19-6204","doi":"https://doi.org/10.18653/v1/d19-6204","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-6204","pdf_url":"https://www.aclweb.org/anthology/D19-6204.pdf","source":{"id":"https://openalex.org/S7407087468","display_name":"International Workshop on Health Text Mining and Information Analysis (LOUHI)","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":"conference"},"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 Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.aclweb.org/anthology/D19-6204.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070880857","display_name":"Diya Li","orcid":"https://orcid.org/0000-0002-3287-9385","observed_orcids":["https://orcid.org/0000-0002-3287-9385"]},"institutions":[{"id":"https://openalex.org/I165799507","display_name":"Rensselaer Polytechnic Institute","ror":"https://ror.org/01rtyzb94","country_code":"US","type":"education","lineage":["https://openalex.org/I165799507"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diya Li","raw_affiliation_strings":["Computer Science Department, Rensselaer Polytechnic Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, Rensselaer Polytechnic Institute","institution_ids":["https://openalex.org/I165799507"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075033889","display_name":"Heng Ji","orcid":"https://orcid.org/0000-0002-0464-7966","observed_orcids":["https://orcid.org/0000-0002-0464-7966"]},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Heng Ji","raw_affiliation_strings":["Department of Computer Science","Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science","institution_ids":[]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075033889"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":3.3454,"has_fulltext":true,"cited_by_count":36,"citation_normalized_percentile":{"value":0.94086022,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":92,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"28","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9926999807357788,"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":0.9926999807357788,"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":0.0026000000070780516,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0006000000284984708,"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/biomedical-relation-extraction","display_name":"biomedical relation extraction","score":0.9990000128746033},{"id":"https://openalex.org/keywords/graph-convolutional-networks","display_name":"graph convolutional networks","score":0.9589999914169312},{"id":"https://openalex.org/keywords/dependency-parsing","display_name":"dependency parsing","score":0.8259999752044678},{"id":"https://openalex.org/keywords/contextualized-embeddings","display_name":"contextual embeddings","score":0.9739999771118164},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"multi-task learning","score":0.984000027179718},{"id":"https://openalex.org/keywords/focal-loss","display_name":"focal loss","score":0.8420000076293945},{"id":"https://openalex.org/keywords/class-imbalance","display_name":"class imbalance","score":0.8370000123977661},{"id":"https://openalex.org/keywords/drug-drug-interaction-extraction","display_name":"drug-drug interaction extraction","score":0.9259999990463257},{"id":"https://openalex.org/keywords/syntactic-context","display_name":"syntactic context","score":0.5920000076293945},{"id":"https://openalex.org/keywords/entity-mentions","display_name":"entity mentions","score":0.5249999761581421}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.86932373046875},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.7969965934753418},{"id":"https://openalex.org/C164883195","wikidata":"https://www.wikidata.org/wiki/Q674834","display_name":"Dependency grammar","level":3,"score":0.6712580323219299},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6166836023330688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.578650951385498},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.568179726600647},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5555668473243713},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.5083803534507751},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5054543018341064},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.48865798115730286},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4885891377925873},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4851192235946655},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.46303829550743103},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.45693153142929077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40577569603919983},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2374958097934723},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.23106089234352112},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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/d19-6204","doi":"https://doi.org/10.18653/v1/d19-6204","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-6204","pdf_url":"https://www.aclweb.org/anthology/D19-6204.pdf","source":{"id":"https://openalex.org/S7407087468","display_name":"International Workshop on Health Text Mining and Information Analysis (LOUHI)","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":"conference"},"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 Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d19-6204","doi":"https://doi.org/10.18653/v1/d19-6204","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-6204","pdf_url":"https://www.aclweb.org/anthology/D19-6204.pdf","source":{"id":"https://openalex.org/S7407087468","display_name":"International Workshop on Health Text Mining and Information Analysis (LOUHI)","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":"conference"},"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 Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4291750258","display_name":"CAREER: Mining and Exploring Heterogeneous Information Networks with Social Factors","funder_award_id":"1741634","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2984684268.pdf","grobid_xml":"https://content.openalex.org/works/W2984684268.grobid-xml"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W1994863898","https://openalex.org/W2067704478","https://openalex.org/W2112796928","https://openalex.org/W2170189740","https://openalex.org/W2250861254","https://openalex.org/W2264517602","https://openalex.org/W2307935645","https://openalex.org/W2485374661","https://openalex.org/W2517194566","https://openalex.org/W2519887557","https://openalex.org/W2525778437","https://openalex.org/W2560748602","https://openalex.org/W2600702321","https://openalex.org/W2611162052","https://openalex.org/W2612233603","https://openalex.org/W2626778328","https://openalex.org/W2765742249","https://openalex.org/W2785001576","https://openalex.org/W2790602452","https://openalex.org/W2884561390","https://openalex.org/W2892094955","https://openalex.org/W2896457183","https://openalex.org/W2910406387","https://openalex.org/W2911489562","https://openalex.org/W2941760716","https://openalex.org/W2963078194","https://openalex.org/W2963341956","https://openalex.org/W2963351448","https://openalex.org/W2963613359","https://openalex.org/W2964026782","https://openalex.org/W2989713338","https://openalex.org/W3152893301"],"related_works":["https://openalex.org/W4205103391","https://openalex.org/W4384563367","https://openalex.org/W4401971159","https://openalex.org/W2252979829","https://openalex.org/W4386485613","https://openalex.org/W4391427521","https://openalex.org/W3129032870","https://openalex.org/W3017538732","https://openalex.org/W2912193388","https://openalex.org/W4313892698"],"abstract_inverted_index":{"In":[0],"this":[1,59],"paper":[2],"we":[3],"tackle":[4],"two":[5,20,110],"unique":[6],"challenges":[7],"in":[8],"biomedical":[9],"relation":[10,84],"extraction.The":[11],"first":[12],"challenge":[13,49],"is":[14,50],"that":[15,35,51,114],"the":[16,54,121],"contextual":[17,46],"information":[18],"between":[19],"entity":[21],"mentions":[22],"often":[23],"involves":[24],"sophisticated":[25],"syntactic":[26],"structures.We":[27],"propose":[28,76],"a":[29,77,98],"novel":[30],"graph":[31],"convolutional":[32],"networks":[33],"model":[34,83,116],"incorporates":[36],"dependency":[37],"parsing":[38],"and":[39,86,96],"contextualized":[40],"embedding":[41],"to":[42,81,89,101],"effectively":[43],"capture":[44],"comprehensive":[45],"information.The":[47],"second":[48],"most":[52],"of":[53],"benchmark":[55],"data":[56],"sets":[57],"for":[58],"task":[60],"are":[61,70],"quite":[62],"imbalanced":[63],"because":[64],"more":[65],"than":[66],"80%":[67],"mention":[68,106],"pairs":[69],"negative":[71],"instances":[72],"(i.e.,":[73],"no":[74],"relations).We":[75],"multi-task":[78],"learning":[79],"framework":[80],"jointly":[82],"identification":[85],"classification":[87],"tasks":[88],"propagate":[90],"supervision":[91],"signals":[92],"from":[93],"each":[94],"other":[95],"apply":[97],"focal":[99],"loss":[100],"focus":[102],"training":[103],"on":[104,120],"ambiguous":[105],"pairs.By":[107],"applying":[108],"these":[109],"strategies,":[111],"experiments":[112],"show":[113],"our":[115],"achieves":[117],"state-of-the-art":[118],"F-score":[119],"2013":[122],"drug-drug":[123],"interaction":[124],"extraction":[125],"task.":[126]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":9}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-11-22T00:00:00"}