{"id":"https://openalex.org/W2740765036","doi":"https://doi.org/10.18653/v1/p17-1044","title":"Deep Semantic Role Labeling: What Works and What\u2019s Next","display_name":"Deep Semantic Role Labeling: What Works and What\u2019s Next","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2740765036","doi":"https://doi.org/10.18653/v1/p17-1044","mag":"2740765036"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p17-1044","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-1044","pdf_url":"https://www.aclweb.org/anthology/P17-1044.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 55th Annual Meeting of the Association for\n          Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P17-1044.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025887224","display_name":"Luheng He","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":"Luheng He","raw_affiliation_strings":["School of Computer Science & Engineering, Univ. of Washington, Seattle, WA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science & Engineering, Univ. of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081862885","display_name":"Kenton Lee","orcid":"https://orcid.org/0000-0002-9534-5970"},"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":"Kenton Lee","raw_affiliation_strings":["School of Computer Science & Engineering, Univ. of Washington, Seattle, WA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science & Engineering, Univ. of Washington, Seattle, WA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004412943","display_name":"Mike Lewis","orcid":"https://orcid.org/0000-0003-0679-6612"},"institutions":[{"id":"https://openalex.org/I4210099336","display_name":"Menlo School","ror":"https://ror.org/01240pn49","country_code":"US","type":"education","lineage":["https://openalex.org/I4210099336"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mike Lewis","raw_affiliation_strings":["Facebook AI Research, Menlo Park, CA"],"affiliations":[{"raw_affiliation_string":"Facebook AI Research, Menlo Park, CA","institution_ids":["https://openalex.org/I4210114444","https://openalex.org/I4210099336"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067919401","display_name":"Luke Zettlemoyer","orcid":"https://orcid.org/0009-0008-8296-0764"},"institutions":[{"id":"https://openalex.org/I4210156221","display_name":"Allen Institute for Artificial Intelligence","ror":"https://ror.org/05w520734","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210156221"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luke Zettlemoyer","raw_affiliation_strings":["Allen Institute for Artificial Intelligence, Seattle, WA"],"affiliations":[{"raw_affiliation_string":"Allen Institute for Artificial Intelligence, Seattle, WA","institution_ids":["https://openalex.org/I4210156221"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5025887224"],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":65.6841,"has_fulltext":true,"cited_by_count":452,"citation_normalized_percentile":{"value":0.99895278,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"473","last_page":"483"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/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/T13629","display_name":"Text Readability and Simplification","score":0.9746999740600586,"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":0.8740302324295044},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.7832903861999512},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6633161306381226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6328072547912598},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6308634877204895},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.6200032234191895},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.549765944480896},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5393005013465881},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.4631670415401459},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45312029123306274},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.44721272587776184},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3286912441253662},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1936722695827484},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.08876368403434753}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8740302324295044},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7832903861999512},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6633161306381226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6328072547912598},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6308634877204895},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6200032234191895},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.549765944480896},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5393005013465881},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.4631670415401459},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45312029123306274},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.44721272587776184},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3286912441253662},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1936722695827484},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.08876368403434753},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p17-1044","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-1044","pdf_url":"https://www.aclweb.org/anthology/P17-1044.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 55th Annual Meeting of the Association for\n          Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p17-1044","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p17-1044","pdf_url":"https://www.aclweb.org/anthology/P17-1044.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 55th Annual Meeting of the Association for\n          Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1746478","display_name":null,"funder_award_id":"FA8750-13-2-0019","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G290391106","display_name":null,"funder_award_id":"FA8750-13-2-001","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G3366966419","display_name":null,"funder_award_id":"W911NF-16-1","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"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"},{"id":"https://openalex.org/G5524522455","display_name":null,"funder_award_id":"DARPA","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G6497436218","display_name":"CAREER: Learning Scalable Models for Grounded Semantic Parsing","funder_award_id":"1252835","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G657448715","display_name":null,"funder_award_id":"W911NF-16-1-","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7750423253","display_name":null,"funder_award_id":"IIS-1562364","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8533175095","display_name":"RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data","funder_award_id":"1562364","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8998121839","display_name":null,"funder_award_id":"911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2740765036.pdf","grobid_xml":"https://content.openalex.org/works/W2740765036.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W168425931","https://openalex.org/W1026270304","https://openalex.org/W1535015163","https://openalex.org/W2038324640","https://openalex.org/W2091896764","https://openalex.org/W2103076621","https://openalex.org/W2114590102","https://openalex.org/W2116064496","https://openalex.org/W2125930537","https://openalex.org/W2126851059","https://openalex.org/W2137132216","https://openalex.org/W2153813398","https://openalex.org/W2158899491","https://openalex.org/W2172176372","https://openalex.org/W2212703438","https://openalex.org/W2250539671","https://openalex.org/W2250726251","https://openalex.org/W2250748818","https://openalex.org/W2251599843","https://openalex.org/W2251668285","https://openalex.org/W2397198482","https://openalex.org/W2553280805","https://openalex.org/W2564486991","https://openalex.org/W2575480513","https://openalex.org/W2915816387","https://openalex.org/W2950621961","https://openalex.org/W2952230511","https://openalex.org/W2962836003","https://openalex.org/W2962977959","https://openalex.org/W2963266340","https://openalex.org/W2963435215","https://openalex.org/W2964084166","https://openalex.org/W4320013820"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W3095877357","https://openalex.org/W183202219","https://openalex.org/W10861731","https://openalex.org/W2072565696","https://openalex.org/W2050451745"],"abstract_inverted_index":{"We":[0,30],"introduce":[1],"a":[2,32,42,72],"new":[3],"deep":[4,33,93],"learning":[5],"model":[6,55],"for":[7,48,114],"semantic":[8],"role":[9],"labeling":[10],"(SRL)":[11],"that":[12,91,109],"significantly":[13],"improves":[14],"the":[15,18,60,78,82],"state":[16,80],"of":[17,44,81,87],"art,":[19],"along":[20],"with":[21,37],"detailed":[22],"analyses":[23],"to":[24,117],"reveal":[25],"its":[26],"strengths":[27],"and":[28,50,65,107],"limitations.":[29],"use":[31],"highway":[34],"BiLSTM":[35],"architecture":[36],"constrained":[38],"decoding,":[39],"while":[40],"observing":[41],"number":[43],"recent":[45],"best":[46],"practices":[47],"initialization":[49],"regularization.":[51],"Our":[52],"8-layer":[53],"ensemble":[54],"achieves":[56],"83.2":[57],"F1":[58,67],"on":[59,68],"CoNLL":[61,69],"2005":[62],"test":[63],"set":[64],"83.4":[66],"2012,":[70],"roughly":[71],"10%":[73],"relative":[74],"error":[75],"reduction":[76],"over":[77],"previous":[79],"art.":[83],"Extensive":[84],"empirical":[85],"analysis":[86],"these":[88,119],"gains":[89],"show":[90],"(1)":[92],"models":[94],"excel":[95],"at":[96],"recovering":[97],"long-distance":[98],"dependencies":[99],"but":[100],"can":[101],"still":[102,112],"make":[103],"surprisingly":[104],"obvious":[105],"errors,":[106],"(2)":[108],"there":[110],"is":[111],"room":[113],"syntactic":[115],"parsers":[116],"improve":[118],"results.":[120]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":26},{"year":2022,"cited_by_count":29},{"year":2021,"cited_by_count":60},{"year":2020,"cited_by_count":97},{"year":2019,"cited_by_count":120},{"year":2018,"cited_by_count":89},{"year":2017,"cited_by_count":9}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}