{"id":"https://openalex.org/W2759211898","doi":"https://doi.org/10.18653/v1/d17-1004","title":"Position-aware Attention and Supervised Data Improve Slot Filling","display_name":"Position-aware Attention and Supervised Data Improve Slot Filling","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2759211898","doi":"https://doi.org/10.18653/v1/d17-1004","mag":"2759211898"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d17-1004","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1004","pdf_url":"https://www.aclweb.org/anthology/D17-1004.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 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D17-1004.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100349352","display_name":"Yuhao Zhang","orcid":"https://orcid.org/0000-0002-9856-436X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuhao Zhang","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077994189","display_name":"Victor W. Zhong","orcid":"https://orcid.org/0000-0001-9208-4683"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Victor Zhong","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051064208","display_name":"Danqi Chen","orcid":"https://orcid.org/0000-0002-6226-6838"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danqi Chen","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074606190","display_name":"Gabor Angeli","orcid":null},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gabor Angeli","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046006076","display_name":"Christopher D. Manning","orcid":"https://orcid.org/0000-0001-6155-649X"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher D. Manning","raw_affiliation_strings":["Stanford University Stanford, CA 94305"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stanford University Stanford, CA 94305","institution_ids":["https://openalex.org/I97018004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I97018004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":841,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"35","last_page":"45"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9948999881744385,"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"}},"topics":[{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9919999837875366,"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"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/relationship-extraction","display_name":"Relationship extraction","score":0.8442478179931641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8115673065185547},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.7173026204109192},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.6342190504074097},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.5812464356422424},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5346011519432068},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.5251493453979492},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.48967722058296204},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.45485663414001465},{"id":"https://openalex.org/keywords/position-paper","display_name":"Position paper","score":0.4516928195953369},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.4385475516319275},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4256313443183899},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.416140079498291},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.4152058959007263},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.06822231411933899}],"concepts":[{"id":"https://openalex.org/C153604712","wikidata":"https://www.wikidata.org/wiki/Q7310755","display_name":"Relationship extraction","level":3,"score":0.8442478179931641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8115673065185547},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.7173026204109192},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.6342190504074097},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.5812464356422424},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5346011519432068},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.5251493453979492},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.48967722058296204},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.45485663414001465},{"id":"https://openalex.org/C78780964","wikidata":"https://www.wikidata.org/wiki/Q7233193","display_name":"Position paper","level":2,"score":0.4516928195953369},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.4385475516319275},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4256313443183899},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.416140079498291},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.4152058959007263},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.06822231411933899},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d17-1004","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1004","pdf_url":"https://www.aclweb.org/anthology/D17-1004.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 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d17-1004","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d17-1004","pdf_url":"https://www.aclweb.org/anthology/D17-1004.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 2017 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320317052","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2759211898.pdf","grobid_xml":"https://content.openalex.org/works/W2759211898.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W59466250","https://openalex.org/W174427690","https://openalex.org/W1551842868","https://openalex.org/W1591801644","https://openalex.org/W1604644367","https://openalex.org/W1838058638","https://openalex.org/W2032566933","https://openalex.org/W2064675550","https://openalex.org/W2095705004","https://openalex.org/W2099779943","https://openalex.org/W2107598941","https://openalex.org/W2123442489","https://openalex.org/W2129842875","https://openalex.org/W2133564696","https://openalex.org/W2138627627","https://openalex.org/W2146502635","https://openalex.org/W2151048449","https://openalex.org/W2158899491","https://openalex.org/W2162590473","https://openalex.org/W2167187514","https://openalex.org/W2181042685","https://openalex.org/W2181629536","https://openalex.org/W2187647469","https://openalex.org/W2236688737","https://openalex.org/W2250521169","https://openalex.org/W2250539671","https://openalex.org/W2251622960","https://openalex.org/W2251847161","https://openalex.org/W2252031764","https://openalex.org/W2301241615","https://openalex.org/W2491040094","https://openalex.org/W2513378248","https://openalex.org/W2517194566","https://openalex.org/W2805259170","https://openalex.org/W2805756377","https://openalex.org/W2806873315","https://openalex.org/W2807441601","https://openalex.org/W2917198466","https://openalex.org/W2952230511","https://openalex.org/W2962785888","https://openalex.org/W2964217331","https://openalex.org/W2964308564","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2352298027","https://openalex.org/W4319940250","https://openalex.org/W842810586","https://openalex.org/W2092919065","https://openalex.org/W3138801416","https://openalex.org/W2444550338","https://openalex.org/W2369351710","https://openalex.org/W2594363579","https://openalex.org/W2169232658","https://openalex.org/W2315233710"],"abstract_inverted_index":{"Organized":[0],"relational":[1],"knowledge":[2,19],"in":[3],"the":[4,15,110,118,124],"form":[5,58],"of":[6,59,93,123],"\"knowledge":[7],"graphs\"":[8],"is":[9,64],"important":[10],"for":[11],"many":[12],"applications.":[13],"However,":[14],"ability":[16],"to":[17,67,140],"populate":[18],"bases":[20],"with":[21,56],"facts":[22],"automatically":[23],"extracted":[24],"from":[25,138],"documents":[26],"has":[27],"improved":[28],"frustratingly":[29],"slowly.":[30],"This":[31],"paper":[32],"simultaneously":[33],"addresses":[34],"two":[35],"issues":[36],"that":[37,63],"have":[38],"held":[39],"back":[40],"prior":[41],"work.":[42],"We":[43],"first":[44],"propose":[45],"an":[46,52],"effective":[47],"new":[48,115],"model,":[49],"which":[50],"combines":[51],"LSTM":[53],"sequence":[54],"model":[55,102,111],"a":[57,74,98],"entity":[60],"position-aware":[61],"attention":[62],"better":[65,94,105],"suited":[66],"relation":[68,79,106,120],"extraction.":[69],"Then":[70],"we":[71],"build":[72],"TACRED,":[73],"large":[75],"(119,474":[76],"examples)":[77],"supervised":[78,95],"extraction":[80,107,121],"dataset,":[81],"obtained":[82],"via":[83],"crowdsourcing":[84],"and":[85,97],"targeted":[86],"towards":[87],"TAC":[88,126],"KBP":[89,127],"relations.":[90],"The":[91],"combination":[92],"data":[96],"more":[99],"appropriate":[100],"high-capacity":[101],"enables":[103],"much":[104],"performance.":[108],"When":[109],"trained":[112],"on":[113],"this":[114],"dataset":[116],"replaces":[117],"previous":[119],"component":[122],"best":[125],"2015":[128],"slot":[129],"filling":[130],"system,":[131],"its":[132],"F":[133],"1":[134],"score":[135],"increases":[136],"markedly":[137],"22.2%":[139],"26.7%.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":22},{"year":2025,"cited_by_count":66},{"year":2024,"cited_by_count":94},{"year":2023,"cited_by_count":154},{"year":2022,"cited_by_count":130},{"year":2021,"cited_by_count":176},{"year":2020,"cited_by_count":115},{"year":2019,"cited_by_count":64},{"year":2018,"cited_by_count":20}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}
