{"id":"https://openalex.org/W2911610808","doi":"https://doi.org/10.1017/s1351324920000224","title":"Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks","display_name":"Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks","publication_year":2020,"publication_date":"2020-05-27","ids":{"openalex":"https://openalex.org/W2911610808","doi":"https://doi.org/10.1017/s1351324920000224","mag":"2911610808"},"language":"en","primary_location":{"id":"doi:10.1017/s1351324920000224","is_oa":true,"landing_page_url":"https://doi.org/10.1017/s1351324920000224","pdf_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/9341ECA9B562DAF55E2F3F966554A667/S1351324920000224a.pdf/div-class-title-universal-lemmatizer-a-sequence-to-sequence-model-for-lemmatizing-universal-dependencies-treebanks-div.pdf","source":{"id":"https://openalex.org/S18088403","display_name":"Natural Language Engineering","issn_l":"1351-3249","issn":["1351-3249","1469-8110"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Natural Language Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/9341ECA9B562DAF55E2F3F966554A667/S1351324920000224a.pdf/div-class-title-universal-lemmatizer-a-sequence-to-sequence-model-for-lemmatizing-universal-dependencies-treebanks-div.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048932608","display_name":"Jenna Kanerva","orcid":"https://orcid.org/0000-0003-4580-5366"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]},{"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":["FI","US"],"is_corresponding":false,"raw_author_name":"Jenna Kanerva","raw_affiliation_strings":[", of , University of , ,","University of Turku"],"raw_orcid":"https://orcid.org/0000-0003-4580-5366","affiliations":[{"raw_affiliation_string":", of , University of , ,","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Turku","institution_ids":["https://openalex.org/I155660961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019929457","display_name":"Filip Ginter","orcid":"https://orcid.org/0000-0002-5484-6103"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]},{"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":["FI","US"],"is_corresponding":false,"raw_author_name":"Filip Ginter","raw_affiliation_strings":[", of , University of , ,","University of Turku"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":", of , University of , ,","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Turku","institution_ids":["https://openalex.org/I155660961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105839982","display_name":"Tapio Salakoski","orcid":null},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]},{"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":["FI","US"],"is_corresponding":false,"raw_author_name":"Tapio Salakoski","raw_affiliation_strings":[", of , University of , ,","University of Turku"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":", of , University of , ,","institution_ids":["https://openalex.org/I201448701"]},{"raw_affiliation_string":"University of Turku","institution_ids":["https://openalex.org/I155660961"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1343,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55303897,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"27","issue":"5","first_page":"545","last_page":"574"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/T10028","display_name":"Topic Modeling","score":0.9991000294685364,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9904000163078308,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.767704963684082},{"id":"https://openalex.org/keywords/lemmatisation","display_name":"Lemmatisation","score":0.7517830729484558},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6501289010047913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6405844688415527},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6364065408706665},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.6172139048576355},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5838870406150818},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5793801546096802},{"id":"https://openalex.org/keywords/lemma","display_name":"Lemma (botany)","score":0.5375665426254272},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.511744499206543},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5003058910369873},{"id":"https://openalex.org/keywords/treebank","display_name":"Treebank","score":0.42775315046310425},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.13009405136108398},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11655312776565552}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.767704963684082},{"id":"https://openalex.org/C161831844","wikidata":"https://www.wikidata.org/wiki/Q2554325","display_name":"Lemmatisation","level":2,"score":0.7517830729484558},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6501289010047913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6405844688415527},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6364065408706665},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.6172139048576355},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5838870406150818},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5793801546096802},{"id":"https://openalex.org/C2777759810","wikidata":"https://www.wikidata.org/wiki/Q149316","display_name":"Lemma (botany)","level":3,"score":0.5375665426254272},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.511744499206543},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5003058910369873},{"id":"https://openalex.org/C206134035","wikidata":"https://www.wikidata.org/wiki/Q811525","display_name":"Treebank","level":3,"score":0.42775315046310425},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.13009405136108398},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11655312776565552},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C46757340","wikidata":"https://www.wikidata.org/wiki/Q43238","display_name":"Poaceae","level":2,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1017/s1351324920000224","is_oa":true,"landing_page_url":"https://doi.org/10.1017/s1351324920000224","pdf_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/9341ECA9B562DAF55E2F3F966554A667/S1351324920000224a.pdf/div-class-title-universal-lemmatizer-a-sequence-to-sequence-model-for-lemmatizing-universal-dependencies-treebanks-div.pdf","source":{"id":"https://openalex.org/S18088403","display_name":"Natural Language Engineering","issn_l":"1351-3249","issn":["1351-3249","1469-8110"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Natural Language Engineering","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1902.00972","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1902.00972","pdf_url":"https://arxiv.org/pdf/1902.00972","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2911610808","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1902.00972v2","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1902.00972","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1902.00972","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1017/s1351324920000224","is_oa":true,"landing_page_url":"https://doi.org/10.1017/s1351324920000224","pdf_url":"https://www.cambridge.org/core/services/aop-cambridge-core/content/view/9341ECA9B562DAF55E2F3F966554A667/S1351324920000224a.pdf/div-class-title-universal-lemmatizer-a-sequence-to-sequence-model-for-lemmatizing-universal-dependencies-treebanks-div.pdf","source":{"id":"https://openalex.org/S18088403","display_name":"Natural Language Engineering","issn_l":"1351-3249","issn":["1351-3249","1469-8110"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311721","host_organization_name":"Cambridge University Press","host_organization_lineage":["https://openalex.org/P4310311721","https://openalex.org/P4310311702"],"host_organization_lineage_names":["Cambridge University Press","University of Cambridge"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Natural Language Engineering","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2911610808.pdf","grobid_xml":"https://content.openalex.org/works/W2911610808.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W13674071","https://openalex.org/W107550075","https://openalex.org/W1582588624","https://openalex.org/W1902237438","https://openalex.org/W1986361898","https://openalex.org/W2097835057","https://openalex.org/W2100664567","https://openalex.org/W2106593913","https://openalex.org/W2118434577","https://openalex.org/W2251386579","https://openalex.org/W2552110825","https://openalex.org/W2574182335","https://openalex.org/W2574640638","https://openalex.org/W2579343286","https://openalex.org/W2605928605","https://openalex.org/W2626905118","https://openalex.org/W2673116624","https://openalex.org/W2739827909","https://openalex.org/W2739861331","https://openalex.org/W2740599975","https://openalex.org/W2740840489","https://openalex.org/W2741095062","https://openalex.org/W2759395805","https://openalex.org/W2760070980","https://openalex.org/W2772302643","https://openalex.org/W2775682630","https://openalex.org/W2804387108","https://openalex.org/W2889510677","https://openalex.org/W2895891096","https://openalex.org/W2898796642","https://openalex.org/W2898879711","https://openalex.org/W2899024931","https://openalex.org/W2918045979","https://openalex.org/W2952233506","https://openalex.org/W2962680795","https://openalex.org/W2962784628","https://openalex.org/W2963212250","https://openalex.org/W2963472176","https://openalex.org/W2963571341","https://openalex.org/W2963714641","https://openalex.org/W2964121744","https://openalex.org/W2988304195","https://openalex.org/W2995104523","https://openalex.org/W3088675891","https://openalex.org/W4238475410","https://openalex.org/W6631190155","https://openalex.org/W6688866165"],"related_works":["https://openalex.org/W2574766132","https://openalex.org/W3094306141","https://openalex.org/W2927851123","https://openalex.org/W2972874257","https://openalex.org/W3098265177","https://openalex.org/W1974551340","https://openalex.org/W2336840621","https://openalex.org/W2426917359","https://openalex.org/W2986195527","https://openalex.org/W1582588624","https://openalex.org/W2948641826","https://openalex.org/W2399695133","https://openalex.org/W1848002569","https://openalex.org/W1986361898","https://openalex.org/W2914021226","https://openalex.org/W2742917097","https://openalex.org/W2757814986","https://openalex.org/W2138321806","https://openalex.org/W2250296256","https://openalex.org/W813954596"],"abstract_inverted_index":{"Abstract":[0],"In":[1,21],"this":[2],"paper,":[3],"we":[4,94],"present":[5],"a":[6,12,34,48,54,71,169],"novel":[7],"lemmatization":[8],"method":[9],"based":[10,36],"on":[11,37,116,145,153],"sequence-to-sequence":[13],"neural":[14],"network":[15],"architecture":[16],"and":[17,42,86,104,120],"morphosyntactic":[18,44,68],"context":[19,57,83],"representation.":[20],"the":[22,29,38,67,82,135,172,175],"proposed":[23],"method,":[24],"our":[25,114,126,141],"context-sensitive":[26],"lemmatizer":[27,115,159],"generates":[28],"lemma":[30,78],"one":[31],"character":[32],"at":[33],"time":[35],"surface":[39],"form":[40],"characters":[41],"its":[43],"features":[45,69],"obtained":[46],"from":[47,60],"morphological":[49,105],"tagger.":[50],"We":[51,112],"argue":[52],"that":[53,125],"sliding":[55],"window":[56],"representation":[58,84],"suffers":[59],"sparseness,":[61],"while":[62,80],"in":[63],"majority":[64],"of":[65,70,148,171],"cases":[66],"word":[72],"bring":[73],"enough":[74],"information":[75],"to":[76,134],"resolve":[77],"ambiguities":[79],"keeping":[81],"dense":[85],"more":[87],"practical":[88],"for":[89,109],"machine":[90],"learning":[91],"systems.":[92,132],"Additionally,":[93],"study":[95],"two":[96],"different":[97,118,122],"data":[98],"augmentation":[99],"methods":[100],"utilizing":[101],"autoencoder":[102],"training":[103],"transducers":[106],"especially":[107],"beneficial":[108],"low-resource":[110],"languages.":[111],"evaluate":[113],"52":[117],"languages":[119],"76":[121,149],"treebanks,":[123],"showing":[124],"system":[127,142],"outperforms":[128,143],"all":[129,162],"latest":[130],"baseline":[131],"Compared":[133],"best":[136],"overall":[137],"baseline,":[138],"UDPipe":[139],"Future,":[140],"it":[144],"62":[146],"out":[147],"treebanks":[150],"reducing":[151],"errors":[152],"average":[154],"by":[155],"19%":[156],"relative.":[157],"The":[158],"together":[160],"with":[161],"trained":[163],"models":[164],"is":[165],"made":[166],"available":[167],"as":[168],"part":[170],"Turku-neural-parsing-pipeline":[173],"under":[174],"Apache":[176],"2.0":[177],"license.":[178]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}