{"id":"https://openalex.org/W2898916575","doi":"https://doi.org/10.18653/v1/w18-5619","title":"Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction","display_name":"Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2898916575","doi":"https://doi.org/10.18653/v1/w18-5619","mag":"2898916575"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w18-5619","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-5619","pdf_url":"https://www.aclweb.org/anthology/W18-5619.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 Ninth International Workshop on Health Text Mining and Information Analysis","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W18-5619.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100707569","display_name":"Chen Lin","orcid":"https://orcid.org/0000-0002-2275-997X"},"institutions":[{"id":"https://openalex.org/I1288882113","display_name":"Boston Children's Hospital","ror":"https://ror.org/00dvg7y05","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1288882113"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Lin","raw_affiliation_strings":["Boston Children's Hospital Informatics Program, Harvard Medical School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston Children's Hospital Informatics Program, Harvard Medical School","institution_ids":["https://openalex.org/I1288882113","https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022326820","display_name":"Timothy M. Miller","orcid":"https://orcid.org/0000-0002-3424-5511"},"institutions":[{"id":"https://openalex.org/I1288882113","display_name":"Boston Children's Hospital","ror":"https://ror.org/00dvg7y05","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1288882113"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Timothy Miller","raw_affiliation_strings":["Boston Children's Hospital Informatics Program, Harvard Medical School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston Children's Hospital Informatics Program, Harvard Medical School","institution_ids":["https://openalex.org/I1288882113","https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054031099","display_name":"Dmitriy Dligach","orcid":"https://orcid.org/0000-0002-2585-2707"},"institutions":[{"id":"https://openalex.org/I1925986","display_name":"Loyola University Chicago","ror":"https://ror.org/04b6x2g63","country_code":"US","type":"education","lineage":["https://openalex.org/I1925986"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitriy Dligach","raw_affiliation_strings":["Loyola University Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Loyola University Chicago","institution_ids":["https://openalex.org/I1925986"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074007015","display_name":"Hadi Amiri","orcid":"https://orcid.org/0000-0003-3278-0729"},"institutions":[{"id":"https://openalex.org/I1288882113","display_name":"Boston Children's Hospital","ror":"https://ror.org/00dvg7y05","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1288882113"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hadi Amiri","raw_affiliation_strings":["Boston Children's Hospital Informatics Program, Harvard Medical School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston Children's Hospital Informatics Program, Harvard Medical School","institution_ids":["https://openalex.org/I1288882113","https://openalex.org/I136199984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068978543","display_name":"Steven Bethard","orcid":"https://orcid.org/0000-0001-9560-6491"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Steven Bethard","raw_affiliation_strings":["University of Arizona"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arizona","institution_ids":["https://openalex.org/I138006243"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087865794","display_name":"Guergana Savova","orcid":"https://orcid.org/0000-0002-5887-200X"},"institutions":[{"id":"https://openalex.org/I1288882113","display_name":"Boston Children's Hospital","ror":"https://ror.org/00dvg7y05","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1288882113"]},{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guergana Savova","raw_affiliation_strings":["Boston Children's Hospital Informatics Program, Harvard Medical School"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston Children's Hospital Informatics Program, Harvard Medical School","institution_ids":["https://openalex.org/I1288882113","https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.2102,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.93636356,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"165","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991999864578247,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9987000226974487,"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":0.8336368799209595},{"id":"https://openalex.org/keywords/relationship-extraction","display_name":"Relationship extraction","score":0.8111256957054138},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.7060542702674866},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6414034962654114},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.6350277066230774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5902746319770813},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5900049805641174},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5884820222854614},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5750779509544373},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5496181845664978},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46143102645874023},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44562190771102905},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4439775347709656},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2963678240776062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8336368799209595},{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8111256957054138},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7060542702674866},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6414034962654114},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.6350277066230774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5902746319770813},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5900049805641174},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5884820222854614},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5750779509544373},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5496181845664978},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46143102645874023},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44562190771102905},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4439775347709656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2963678240776062},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/w18-5619","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-5619","pdf_url":"https://www.aclweb.org/anthology/W18-5619.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 Ninth International Workshop on Health Text Mining and Information Analysis","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w18-5619","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w18-5619","pdf_url":"https://www.aclweb.org/anthology/W18-5619.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 Ninth International Workshop on Health Text Mining and Information Analysis","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2508127175","display_name":null,"funder_award_id":"1U24CA184407","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"},{"id":"https://openalex.org/G5529147748","display_name":null,"funder_award_id":"R01LM010090","funder_id":"https://openalex.org/F4320337372","funder_display_name":"U.S. National Library of Medicine"},{"id":"https://openalex.org/G5539889705","display_name":null,"funder_award_id":"R01LM010090","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"}],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"},{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2898916575.pdf","grobid_xml":"https://content.openalex.org/works/W2898916575.grobid-xml"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W30314283","https://openalex.org/W1522301498","https://openalex.org/W1546148720","https://openalex.org/W1565010410","https://openalex.org/W1571872475","https://openalex.org/W1576601469","https://openalex.org/W1876515049","https://openalex.org/W1889268436","https://openalex.org/W1913805045","https://openalex.org/W1924770834","https://openalex.org/W1944671275","https://openalex.org/W1995687988","https://openalex.org/W2061211139","https://openalex.org/W2064675550","https://openalex.org/W2098136027","https://openalex.org/W2101210369","https://openalex.org/W2103931177","https://openalex.org/W2108501770","https://openalex.org/W2108604762","https://openalex.org/W2122294423","https://openalex.org/W2127194753","https://openalex.org/W2130942839","https://openalex.org/W2136502946","https://openalex.org/W2137023796","https://openalex.org/W2137407193","https://openalex.org/W2140234018","https://openalex.org/W2146057216","https://openalex.org/W2146960529","https://openalex.org/W2153579005","https://openalex.org/W2159583324","https://openalex.org/W2159630904","https://openalex.org/W2160815625","https://openalex.org/W2163605009","https://openalex.org/W2169704124","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2250417532","https://openalex.org/W2250646484","https://openalex.org/W2250991932","https://openalex.org/W2251220668","https://openalex.org/W2293262013","https://openalex.org/W2293452551","https://openalex.org/W2384495648","https://openalex.org/W2396881363","https://openalex.org/W2468432491","https://openalex.org/W2509340590","https://openalex.org/W2530816535","https://openalex.org/W2556522401","https://openalex.org/W2608829447","https://openalex.org/W2739896562","https://openalex.org/W2740053148","https://openalex.org/W2741502284","https://openalex.org/W2751696754","https://openalex.org/W2759336060","https://openalex.org/W2917019145","https://openalex.org/W2951970475","https://openalex.org/W2963626623","https://openalex.org/W2963815651","https://openalex.org/W2964121744","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W3032952384","https://openalex.org/W3034302643","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W2964335273","https://openalex.org/W1889624880","https://openalex.org/W2229372569"],"abstract_inverted_index":{"Neural":[0],"network":[1,28],"models":[2],"are":[3],"oftentimes":[4],"restricted":[5],"by":[6],"limited":[7],"labeled":[8],"instances":[9],"and":[10,15,45,53,56],"resort":[11],"to":[12,23],"advanced":[13],"architectures":[14],"features":[16],"for":[17,66,70],"cutting":[18],"edge":[19],"performance.":[20],"We":[21],"propose":[22],"build":[24],"a":[25,35,71],"recurrent":[26],"neural":[27],"with":[29],"multiple":[30],"semantically":[31],"heterogeneous":[32],"embeddings":[33],"within":[34],"self-training":[36],"framework.":[37],"Our":[38],"framework":[39],"makes":[40],"use":[41],"of":[42],"labeled,":[43],"unlabeled,":[44],"social":[46],"media":[47],"data,":[48],"operates":[49],"on":[50],"basic":[51],"features,":[52],"is":[54],"scalable":[55],"generalizable.":[57],"With":[58],"this":[59],"method,":[60],"we":[61],"establish":[62],"the":[63],"state-of-the-art":[64],"result":[65],"both":[67],"in-and":[68],"cross-domain":[69],"clinical":[72],"temporal":[73],"relation":[74],"extraction":[75],"task.":[76]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
