{"id":"https://openalex.org/W4235806404","doi":"https://doi.org/10.1109/ialp.2018.8629257","title":"Tibetan Word Segmentation Method Based on BiLSTM_ CRF Model","display_name":"Tibetan Word Segmentation Method Based on BiLSTM_ CRF Model","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W4235806404","doi":"https://doi.org/10.1109/ialp.2018.8629257"},"language":"en","primary_location":{"id":"doi:10.1109/ialp.2018.8629257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp.2018.8629257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100356430","display_name":"Lili Wang","orcid":"https://orcid.org/0000-0003-3228-4290"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lili Wang","raw_affiliation_strings":["College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032919424","display_name":"Hongwu Yang","orcid":"https://orcid.org/0000-0002-8939-3386"},"institutions":[{"id":"https://openalex.org/I68986083","display_name":"Northwest Normal University","ror":"https://ror.org/00gx3j908","country_code":"CN","type":"education","lineage":["https://openalex.org/I68986083"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwu Yang","raw_affiliation_strings":["College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China","institution_ids":["https://openalex.org/I68986083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100356430"],"corresponding_institution_ids":["https://openalex.org/I68986083"],"apc_list":null,"apc_paid":null,"fwci":1.3031,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.8597809,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"297","last_page":"302"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9976999759674072,"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":0.9976999759674072,"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":0.9750000238418579,"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":0.9721999764442444,"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":0.7608743906021118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.726925253868103},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7171288728713989},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.6378856897354126},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.6279168725013733},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5956946611404419},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.5593967437744141},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.5545069575309753},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5532636642456055},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5457044839859009},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4480573832988739},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4118277132511139},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36783137917518616},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.2289254069328308},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.11326786875724792}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7608743906021118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.726925253868103},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7171288728713989},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.6378856897354126},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.6279168725013733},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5956946611404419},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.5593967437744141},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5545069575309753},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5532636642456055},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5457044839859009},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4480573832988739},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4118277132511139},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36783137917518616},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2289254069328308},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.11326786875724792},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ialp.2018.8629257","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ialp.2018.8629257","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 International Conference on Asian Language Processing (IALP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1614298861","https://openalex.org/W1963938256","https://openalex.org/W2016732758","https://openalex.org/W2064675550","https://openalex.org/W2117248370","https://openalex.org/W2251501517","https://openalex.org/W2333850309","https://openalex.org/W2364697730","https://openalex.org/W2371519299","https://openalex.org/W2376053756","https://openalex.org/W2381896109","https://openalex.org/W6636510571","https://openalex.org/W6654621639","https://openalex.org/W6677608580","https://openalex.org/W6989845895"],"related_works":["https://openalex.org/W2946409105","https://openalex.org/W3152932816","https://openalex.org/W2985392712","https://openalex.org/W4388996947","https://openalex.org/W3133567596","https://openalex.org/W2798009317","https://openalex.org/W4382201653","https://openalex.org/W3203949288","https://openalex.org/W3175524270","https://openalex.org/W4313247739"],"abstract_inverted_index":{"Tibetan":[0,11,15,19,48,67,86,140,151],"word":[1,20,49,107,110,141,152,158,164],"segmentation":[2,50,142,153,159,165],"is":[3,69,125,172,178],"one":[4],"of":[5,27,41,163],"the":[6,25,39,95,101,116,131,139,149,169,175],"key":[7],"technologies":[8],"to":[9,83,105,115,130,137],"realize":[10],"speech":[12,16],"synthesis":[13],"and":[14,29,75,174],"recognition.":[17],"Traditional":[18],"segmentations":[21],"mainly":[22],"relied":[23],"on":[24,53],"combination":[26],"rules":[28],"statistics.":[30],"The":[31,66,88,109,119,144,161],"model":[32,63,124,133],"automatic":[33],"learning":[34],"features":[35,90,112,129],"become":[36],"possible":[37],"in":[38],"era":[40],"deep":[42],"learning.":[43],"This":[44],"paper":[45],"proposes":[46],"a":[47,85],"method":[51,154],"based":[52],"bidirectional":[54],"long-term":[55],"memory":[56],"neural":[57],"network":[58],"with":[59,94],"conditional":[60],"random":[61],"field":[62],"(BiLSTM_":[64],"CRF).":[65],"sentence":[68],"firstly":[70],"divided":[71],"into":[72,100],"clauses,":[73],"words":[74,77,80,99],"abbreviated":[76],"manually.":[78],"Low-frequency":[79],"are":[81,91,113],"removed":[82],"form":[84],"dictionary.":[87],"text":[89],"then":[92],"extracted":[93],"dictionary":[96],"by":[97],"embedding":[98],"corpus":[102],"using":[103],"Word2vec":[104],"get":[106],"vectors.":[108],"vector":[111],"transmited":[114],"BiLSTM":[117,123],"model.":[118],"learned":[120],"result":[121],"from":[122],"finally":[126],"transmitted":[127],"as":[128],"CRF":[132],"for":[134],"four-word":[135],"labeling":[136],"obtain":[138],"results.":[143],"experimental":[145],"results":[146],"show":[147],"that":[148],"proposed":[150],"can":[155,166],"achieve":[156],"better":[157],"effect.":[160],"accuracy":[162],"reach":[167],"94.33%,":[168],"recall":[170],"rate":[171],"93.89%":[173],"F":[176],"value":[177],"94.11%.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-25T21:42:39.735039","created_date":"2025-10-10T00:00:00"}