{"id":"https://openalex.org/W3096498944","doi":"https://doi.org/10.1587/transinf.2020edp7038","title":"Contextualized Character Embedding with Multi-Sequence LSTM for Automatic Word Segmentation","display_name":"Contextualized Character Embedding with Multi-Sequence LSTM for Automatic Word Segmentation","publication_year":2020,"publication_date":"2020-10-31","ids":{"openalex":"https://openalex.org/W3096498944","doi":"https://doi.org/10.1587/transinf.2020edp7038","mag":"3096498944"},"language":"en","primary_location":{"id":"doi:10.1587/transinf.2020edp7038","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2020edp7038","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E103.D/11/E103.D_2020EDP7038/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.jstage.jst.go.jp/article/transinf/E103.D/11/E103.D_2020EDP7038/_pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101735131","display_name":"Hyunyoung Lee","orcid":"https://orcid.org/0000-0003-2553-6576"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyunyoung LEE","raw_affiliation_strings":["Kookmin University"],"affiliations":[{"raw_affiliation_string":"Kookmin University","institution_ids":["https://openalex.org/I110273157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017037508","display_name":"Seung-Shik Kang","orcid":"https://orcid.org/0000-0003-3318-6326"},"institutions":[{"id":"https://openalex.org/I110273157","display_name":"Kookmin University","ror":"https://ror.org/0049erg63","country_code":"KR","type":"education","lineage":["https://openalex.org/I110273157"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seungshik KANG","raw_affiliation_strings":["Kookmin University"],"affiliations":[{"raw_affiliation_string":"Kookmin University","institution_ids":["https://openalex.org/I110273157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101735131"],"corresponding_institution_ids":["https://openalex.org/I110273157"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14055108,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"E103.D","issue":"11","first_page":"2371","last_page":"2378"},"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":0.9998000264167786,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9973000288009644,"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/bigram","display_name":"Bigram","score":0.9127330183982849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8511085510253906},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.74086594581604},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6758161783218384},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6676499843597412},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6581922769546509},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6182137727737427},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6055222749710083},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.6035599112510681},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5697460174560547},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5088251233100891},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5073289275169373},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4769372344017029},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.43479523062705994},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.1639215648174286}],"concepts":[{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.9127330183982849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8511085510253906},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.74086594581604},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6758161783218384},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6676499843597412},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6581922769546509},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6182137727737427},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6055222749710083},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.6035599112510681},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5697460174560547},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5088251233100891},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5073289275169373},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4769372344017029},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.43479523062705994},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.1639215648174286},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C137546455","wikidata":"https://www.wikidata.org/wiki/Q3213474","display_name":"Trigram","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1587/transinf.2020edp7038","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2020edp7038","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E103.D/11/E103.D_2020EDP7038/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1587/transinf.2020edp7038","is_oa":true,"landing_page_url":"https://doi.org/10.1587/transinf.2020edp7038","pdf_url":"https://www.jstage.jst.go.jp/article/transinf/E103.D/11/E103.D_2020EDP7038/_pdf","source":{"id":"https://openalex.org/S2486202937","display_name":"IEICE Transactions on Information and Systems","issn_l":"0916-8532","issn":["0916-8532","1745-1361"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4320800604","host_organization_name":"Institute of Electronics, Information and Communication Engineers","host_organization_lineage":["https://openalex.org/P4320800604"],"host_organization_lineage_names":["Institute of Electronics, Information and Communication Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEICE Transactions on Information and Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1701887901","display_name":null,"funder_award_id":"2017M3C4A7068186","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G342704958","display_name":null,"funder_award_id":"funded","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G982292920","display_name":null,"funder_award_id":"NRF-20","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3096498944.pdf","grobid_xml":"https://content.openalex.org/works/W3096498944.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W1525783482","https://openalex.org/W1533861849","https://openalex.org/W1614298861","https://openalex.org/W1832693441","https://openalex.org/W1902237438","https://openalex.org/W1940872118","https://openalex.org/W1951325712","https://openalex.org/W2036516910","https://openalex.org/W2064675550","https://openalex.org/W2107878631","https://openalex.org/W2117130368","https://openalex.org/W2130942839","https://openalex.org/W2133564696","https://openalex.org/W2140679639","https://openalex.org/W2147880316","https://openalex.org/W2153579005","https://openalex.org/W2170240176","https://openalex.org/W2250539671","https://openalex.org/W2250861254","https://openalex.org/W2251811146","https://openalex.org/W2252225757","https://openalex.org/W2346712328","https://openalex.org/W2394700483","https://openalex.org/W2463895987","https://openalex.org/W2470673105","https://openalex.org/W2493916176","https://openalex.org/W2579285701","https://openalex.org/W2756748969","https://openalex.org/W2757350179","https://openalex.org/W2759827652","https://openalex.org/W2782399326","https://openalex.org/W2798955519","https://openalex.org/W2888358426","https://openalex.org/W2896457183","https://openalex.org/W2905559537","https://openalex.org/W2950577311","https://openalex.org/W2962739339","https://openalex.org/W2962902328","https://openalex.org/W2963026768","https://openalex.org/W2963625095","https://openalex.org/W2963682821","https://openalex.org/W2964308564","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W1700330385","https://openalex.org/W2105076537","https://openalex.org/W2197825247","https://openalex.org/W2041167939","https://openalex.org/W2002221802","https://openalex.org/W2250909759","https://openalex.org/W2562995433","https://openalex.org/W2131111393","https://openalex.org/W1500873938","https://openalex.org/W2020757772"],"abstract_inverted_index":{"Contextual":[0],"information":[1,71,95],"is":[2,41,53,78],"a":[3,63],"crucial":[4],"factor":[5],"in":[6,30,44,49,85],"natural":[7],"language":[8],"processing":[9],"tasks":[10],"such":[11,96],"as":[12,97],"sequence":[13],"labeling.":[14],"Previous":[15],"studies":[16],"on":[17,83],"contextualized":[18,64,81,111,142],"embedding":[19,22,66,112],"and":[20,102,123,147],"word":[21,126,165],"have":[23],"explored":[24],"the":[25,42,46,86,98,110,119,139,156,162,175],"context":[26],"of":[27,36,100,104,113,121,131,141,159,164],"word-level":[28],"tokens":[29],"order":[31],"to":[32,55],"obtain":[33],"useful":[34],"features":[35],"languages.":[37],"However,":[38],"unlike":[39],"it":[40],"case":[43],"English,":[45],"fundamental":[47],"task":[48,87],"East":[50],"Asian":[51],"languages":[52],"related":[54],"character-level":[56],"tokens.":[57],"In":[58],"this":[59],"paper,":[60],"we":[61],"propose":[62],"character":[65,115,133,149],"method":[67,173],"using":[68],"n-gram":[69],"multi-sequences":[70,84],"with":[72,92],"long":[73],"short-term":[74],"memory":[75],"(LSTM).":[76],"It":[77],"hypothesized":[79],"that":[80,109,130,138,170],"embeddings":[82,143],"help":[88],"each":[89],"other":[90],"deal":[91],"long-term":[93],"contextual":[94],"notion":[99,120],"spans":[101,122],"boundaries":[103,124],"segmentation.":[105,166],"The":[106,167],"analysis":[107],"shows":[108],"bigram":[114,148],"sequences":[116,150],"encodes":[117],"well":[118],"for":[125],"segmentation":[127],"rather":[128,154],"than":[129,155],"unigram":[132,146],"sequences.":[134],"We":[135],"find":[136],"out":[137],"combination":[140],"from":[144],"both":[145],"at":[151],"output":[152],"layer":[153,158],"input":[157],"LSTMs":[160],"improves":[161],"performance":[163],"comparison":[168],"showed":[169],"our":[171],"proposed":[172],"outperforms":[174],"previous":[176],"models.":[177]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}