{"id":"https://openalex.org/W2892333784","doi":"https://doi.org/10.18653/v1/d18-1161","title":"Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing","display_name":"Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2892333784","doi":"https://doi.org/10.18653/v1/d18-1161","mag":"2892333784"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1161","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1161","pdf_url":"https://www.aclweb.org/anthology/D18-1161.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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1161.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019810499","display_name":"Daniel Fern\u00e1ndez\u2010Gonz\u00e1lez","orcid":"https://orcid.org/0000-0002-6733-2371"},"institutions":[{"id":"https://openalex.org/I11019714","display_name":"Universidade da Coru\u00f1a","ror":"https://ror.org/01qckj285","country_code":"ES","type":"education","lineage":["https://openalex.org/I11019714"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Daniel Fern\u00e1ndez-Gonz\u00e1lez","raw_affiliation_strings":["Universidade da Coru\u00f1a FASTPARSE Lab, LyS Research Group, Departamento de Computaci\u00f3n Campus de Elvi\u00f1a, s/n, 15071 A Coru\u00f1a, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coru\u00f1a FASTPARSE Lab, LyS Research Group, Departamento de Computaci\u00f3n Campus de Elvi\u00f1a, s/n, 15071 A Coru\u00f1a, Spain","institution_ids":["https://openalex.org/I11019714"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030874155","display_name":"Carlos G\u00f3mez\u2010Rodr\u00edguez","orcid":"https://orcid.org/0000-0003-0752-8812"},"institutions":[{"id":"https://openalex.org/I11019714","display_name":"Universidade da Coru\u00f1a","ror":"https://ror.org/01qckj285","country_code":"ES","type":"education","lineage":["https://openalex.org/I11019714"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Carlos G\u00f3mez-Rodr\u00edguez","raw_affiliation_strings":["Universidade da Coru\u00f1a FASTPARSE Lab, LyS Research Group, Departamento de Computaci\u00f3n Campus de Elvi\u00f1a, s/n, 15071 A Coru\u00f1a, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade da Coru\u00f1a FASTPARSE Lab, LyS Research Group, Departamento de Computaci\u00f3n Campus de Elvi\u00f1a, s/n, 15071 A Coru\u00f1a, Spain","institution_ids":["https://openalex.org/I11019714"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030874155"],"corresponding_institution_ids":["https://openalex.org/I11019714"],"apc_list":null,"apc_paid":null,"fwci":1.3534,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.86066397,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1303","last_page":"1313"},"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":0.9973999857902527,"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/T10015","display_name":"Genomics and Phylogenetic Studies","score":0.982200026512146,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.8609849214553833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8238027095794678},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7321091890335083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5215349197387695},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.4863720238208771},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4197724461555481},{"id":"https://openalex.org/keywords/lr-parser","display_name":"LR parser","score":0.4106020927429199},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3304648995399475},{"id":"https://openalex.org/keywords/parser-combinator","display_name":"Parser combinator","score":0.3002720773220062}],"concepts":[{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.8609849214553833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8238027095794678},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7321091890335083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5215349197387695},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.4863720238208771},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4197724461555481},{"id":"https://openalex.org/C35164859","wikidata":"https://www.wikidata.org/wiki/Q1756442","display_name":"LR parser","level":4,"score":0.4106020927429199},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3304648995399475},{"id":"https://openalex.org/C118364021","wikidata":"https://www.wikidata.org/wiki/Q7139956","display_name":"Parser combinator","level":3,"score":0.3002720773220062},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1161","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1161","pdf_url":"https://www.aclweb.org/anthology/D18-1161.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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1161","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1161","pdf_url":"https://www.aclweb.org/anthology/D18-1161.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 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2198589857","display_name":"Fast Natural Language Parsing for Large-Scale NLP","funder_award_id":"714150","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G3768625766","display_name":null,"funder_award_id":"ED431B 2017/01","funder_id":"https://openalex.org/F4320326655","funder_display_name":"Xunta de Galicia"},{"id":"https://openalex.org/G6258374248","display_name":null,"funder_award_id":"ED431B","funder_id":"https://openalex.org/F4320326655","funder_display_name":"Xunta de Galicia"},{"id":"https://openalex.org/G7842005466","display_name":null,"funder_award_id":"Horizon 2020","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320326655","display_name":"Xunta de Galicia","ror":"https://ror.org/0181xnw06"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2892333784.pdf","grobid_xml":"https://content.openalex.org/works/W2892333784.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W595069947","https://openalex.org/W1632114991","https://openalex.org/W2038754641","https://openalex.org/W2052816566","https://openalex.org/W2069893028","https://openalex.org/W2096204319","https://openalex.org/W2126433015","https://openalex.org/W2251204185","https://openalex.org/W2251823276","https://openalex.org/W2251855586","https://openalex.org/W2293447805","https://openalex.org/W2301603631","https://openalex.org/W2305592425","https://openalex.org/W2515464445","https://openalex.org/W2554915555","https://openalex.org/W2559842427","https://openalex.org/W2563495010","https://openalex.org/W2799211465","https://openalex.org/W2962733492","https://openalex.org/W2963073938","https://openalex.org/W2963084773","https://openalex.org/W2963227939","https://openalex.org/W2963330800","https://openalex.org/W2963372751","https://openalex.org/W2964030814","https://openalex.org/W2964310805"],"related_works":["https://openalex.org/W1971174339","https://openalex.org/W1992279947","https://openalex.org/W2166030873","https://openalex.org/W2296332834","https://openalex.org/W1574037173","https://openalex.org/W4285289289","https://openalex.org/W2145777524","https://openalex.org/W1529591059","https://openalex.org/W6643695","https://openalex.org/W2529664582"],"abstract_inverted_index":{"We":[0],"introduce":[1],"novel":[2],"dynamic":[3,28],"oracles":[4,29],"for":[5,15],"training":[6],"two":[7],"of":[8,52],"the":[9,18,27,50,53,60,77],"most":[10],"accurate":[11],"known":[12],"shiftreduce":[13],"algorithms":[14],"constituent":[16,74],"parsing:":[17],"top-down":[19],"and":[20],"in-order":[21,55],"transition-based":[22],"parsers.":[23],"In":[24,46],"both":[25],"cases,":[26],"manage":[30],"to":[31,38,63],"notably":[32],"increase":[33],"their":[34],"accuracy,":[35],"in":[36],"comparison":[37],"that":[39],"obtained":[40,67],"by":[41,48,68],"performing":[42],"classic":[43],"static":[44],"training.":[45],"addition,":[47],"improving":[49],"performance":[51],"state-of-the-art":[54],"shift-reduce":[56,73],"parser,":[57],"we":[58],"achieve":[59],"best":[61],"accuracy":[62],"date":[64],"(92.0":[65],"F1)":[66],"a":[69],"fullysupervised":[70],"single-model":[71],"greedy":[72],"parser":[75],"on":[76],"WSJ":[78],"benchmark.":[79]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-05-19T08:33:51.333923","created_date":"2025-10-10T00:00:00"}
