{"id":"https://openalex.org/W2963084773","doi":"https://doi.org/10.18653/v1/p18-1249","title":"Constituency Parsing with a Self-Attentive Encoder","display_name":"Constituency Parsing with a Self-Attentive Encoder","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2963084773","doi":"https://doi.org/10.18653/v1/p18-1249","mag":"2963084773"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-1249","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1249","pdf_url":"https://www.aclweb.org/anthology/P18-1249.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 56th Annual Meeting of the Association for 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/P18-1249.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011843361","display_name":"Nikita Kitaev","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nikita Kitaev","raw_affiliation_strings":["Computer Science Division University of California, Berkeley"],"affiliations":[{"raw_affiliation_string":"Computer Science Division University of California, Berkeley","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004921249","display_name":"Dan Klein","orcid":"https://orcid.org/0000-0002-8881-1902"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dan Klein","raw_affiliation_strings":["Computer Science Division University of California, Berkeley"],"affiliations":[{"raw_affiliation_string":"Computer Science Division University of California, Berkeley","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011843361"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":51.9563,"has_fulltext":true,"cited_by_count":487,"citation_normalized_percentile":{"value":0.99859576,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2676","last_page":"2686"},"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":0.9793000221252441,"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/treebank","display_name":"Treebank","score":0.9199779033660889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8632659912109375},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.808316707611084},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.7863073348999023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.724807858467102},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7144257426261902},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6972370147705078},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6118366122245789},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5784239768981934},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5627127885818481},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3860374093055725},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09212437272071838}],"concepts":[{"id":"https://openalex.org/C206134035","wikidata":"https://www.wikidata.org/wiki/Q811525","display_name":"Treebank","level":3,"score":0.9199779033660889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8632659912109375},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.808316707611084},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.7863073348999023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.724807858467102},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7144257426261902},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6972370147705078},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6118366122245789},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5784239768981934},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5627127885818481},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3860374093055725},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09212437272071838},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p18-1249","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1249","pdf_url":"https://www.aclweb.org/anthology/P18-1249.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-1249","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-1249","pdf_url":"https://www.aclweb.org/anthology/P18-1249.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 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G6894402473","display_name":null,"funder_award_id":"Fellowship","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/W2963084773.pdf","grobid_xml":"https://content.openalex.org/works/W2963084773.grobid-xml"},"referenced_works_count":19,"referenced_works":["https://openalex.org/W1869752048","https://openalex.org/W2098021339","https://openalex.org/W2143391265","https://openalex.org/W2250562110","https://openalex.org/W2563495010","https://openalex.org/W2564486991","https://openalex.org/W2739769945","https://openalex.org/W2962733492","https://openalex.org/W2962739339","https://openalex.org/W2963069010","https://openalex.org/W2963073938","https://openalex.org/W2963330800","https://openalex.org/W2963372751","https://openalex.org/W2963403868","https://openalex.org/W2963462075","https://openalex.org/W2963808856","https://openalex.org/W2964030814","https://openalex.org/W4385245566","https://openalex.org/W4394666973"],"related_works":["https://openalex.org/W1043255351","https://openalex.org/W2135057643","https://openalex.org/W1533278948","https://openalex.org/W1781980207","https://openalex.org/W2109902858","https://openalex.org/W2951759144","https://openalex.org/W28706907","https://openalex.org/W2949524199","https://openalex.org/W3101476433","https://openalex.org/W2250979629"],"abstract_inverted_index":{"We":[0],"demonstrate":[1],"that":[2,56],"replacing":[3],"an":[4],"LSTM":[5],"encoder":[6,64],"with":[7],"a":[8,16],"self-attentive":[9],"architecture":[10],"can":[11,65],"lead":[12,66],"to":[13,15,43,67],"improvements":[14],"state-ofthe-art":[17,83],"discriminative":[18],"constituency":[19],"parser.":[20],"The":[21],"use":[22,42,97],"of":[23,98,121],"attention":[24],"makes":[25],"explicit":[26],"the":[27,38,63,90,96,114,122,126],"manner":[28],"in":[29,37,62,125],"which":[30,40],"information":[31,61],"is":[32],"propagated":[33],"between":[34],"different":[35,74],"locations":[36],"sentence,":[39],"we":[41,54,72],"both":[44],"analyze":[45],"our":[46],"model":[47],"and":[48,59,102],"propose":[49],"potential":[50],"improvements.":[51],"For":[52],"example,":[53],"find":[55],"separating":[57],"positional":[58],"content":[60],"improved":[68],"parsing":[69],"accuracy.":[70],"Additionally,":[71],"evaluate":[73],"approaches":[75],"for":[76,85],"lexical":[77],"representation.":[78],"Our":[79,110],"parser":[80,111],"achieves":[81],"new":[82],"results":[84],"single":[86],"models":[87],"trained":[88],"on":[89,119],"Penn":[91],"Treebank:":[92],"93.55":[93],"F1":[94,104],"without":[95],"any":[99],"external":[100],"data,":[101],"95.13":[103],"when":[105],"using":[106],"pre-trained":[107],"word":[108],"representations.":[109],"also":[112],"outperforms":[113],"previous":[115],"best-published":[116],"accuracy":[117],"figures":[118],"8":[120],"9":[123],"languages":[124],"SPMRL":[127],"dataset.":[128]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":23},{"year":2023,"cited_by_count":67},{"year":2022,"cited_by_count":62},{"year":2021,"cited_by_count":102},{"year":2020,"cited_by_count":113},{"year":2019,"cited_by_count":79},{"year":2018,"cited_by_count":13}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}