{"id":"https://openalex.org/W2886517518","doi":"https://doi.org/10.1145/3232116.3232131","title":"Chinese word segmentation model based on BI_GRU_AT_HN_CRF_6","display_name":"Chinese word segmentation model based on BI_GRU_AT_HN_CRF_6","publication_year":2018,"publication_date":"2018-05-19","ids":{"openalex":"https://openalex.org/W2886517518","doi":"https://doi.org/10.1145/3232116.3232131","mag":"2886517518"},"language":"en","primary_location":{"id":"doi:10.1145/3232116.3232131","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3232116.3232131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Intelligent Information Processing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5068340608","display_name":"Ge Yunsheng","orcid":null},"institutions":[{"id":"https://openalex.org/I38706770","display_name":"Guilin University of Technology","ror":"https://ror.org/03z391397","country_code":"CN","type":"education","lineage":["https://openalex.org/I38706770"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Yunsheng","raw_affiliation_strings":["College of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi","institution_ids":["https://openalex.org/I38706770"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007356882","display_name":"Jie Kong","orcid":"https://orcid.org/0000-0002-6997-7536"},"institutions":[{"id":"https://openalex.org/I38706770","display_name":"Guilin University of Technology","ror":"https://ror.org/03z391397","country_code":"CN","type":"education","lineage":["https://openalex.org/I38706770"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kong Jie","raw_affiliation_strings":["College of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Guilin University of Technology, Guilin, Guangxi","institution_ids":["https://openalex.org/I38706770"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I38706770"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"88","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9984999895095825,"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":0.9984999895095825,"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":0.9973000288009644,"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"}},{"id":"https://openalex.org/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.9575999975204468,"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":0.8269083499908447},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.721574604511261},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7061508297920227},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6987919211387634},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5951375961303711},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5906493663787842},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.5899378657341003},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5830088257789612},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5821129083633423},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5575470328330994},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5171520113945007},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45167770981788635},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.44963759183883667},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4336743950843811},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3388093113899231}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8269083499908447},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.721574604511261},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7061508297920227},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6987919211387634},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5951375961303711},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5906493663787842},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.5899378657341003},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5830088257789612},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5821129083633423},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5575470328330994},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5171520113945007},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45167770981788635},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.44963759183883667},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4336743950843811},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3388093113899231},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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:10.1145/3232116.3232131","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3232116.3232131","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 3rd International Conference on Intelligent Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.800000011920929,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1785832192","https://openalex.org/W2149710647","https://openalex.org/W2246094576","https://openalex.org/W2249459753","https://openalex.org/W2346578824","https://openalex.org/W2599962950","https://openalex.org/W2743515774","https://openalex.org/W2790536622","https://openalex.org/W2793115980"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4288108708","https://openalex.org/W4387163678","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2890685186","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W3173606726"],"abstract_inverted_index":{"Chinese":[0,28,92],"word":[1,29,55,93,136],"segmentation":[2,30,56,156],"is":[3,13,173],"an":[4],"indispensable":[5],"step":[6],"in":[7,88,107,113,177],"natural":[8],"language":[9],"processing,":[10],"and":[11,50,53,66,82,121,150,180],"it":[12],"also":[14],"the":[15,21,44,54,60,77,89,96,99,103,108,114,117,126,133,147,152,162,168],"most":[16],"important":[17],"step.":[18],"At":[19],"present,":[20],"use":[22],"of":[23,48,71,91,98,116,135,164,170],"recurrent":[24],"neural":[25,73,154],"network":[26,46,62,74,155],"to":[27,79,140],"model":[31,47],"has":[32],"become":[33],"a":[34,69,174],"new":[35],"trend.":[36],"The":[37],"researchers":[38],"proposed":[39],"various":[40],"models":[41],"based":[42,58],"on":[43,59],"LSTM":[45,65],"long":[49],"short":[51],"memory":[52,85],"method":[57],"GRU":[61,67],"model.":[63,157],"Both":[64],"are":[68],"type":[70],"circulatory":[72],"that":[75,125,160],"inherits":[76],"ability":[78],"automatically":[80],"learn":[81],"long-short":[83],"term":[84],"characteristics.":[86],"However,":[87],"process":[90],"segmentation,":[94],"as":[95],"length":[97],"sentence":[100,128],"becomes":[101,110],"longer,":[102],"inter-dependent":[104],"feature":[105,119,123],"distance":[106],"context":[109],"farther,":[111],"resulting":[112],"loss":[115],"historical":[118],"information":[120,124],"future":[122],"given":[127],"depends":[129],"on,":[130],"thereby":[131],"reducing":[132],"accuracy":[134],"segmentation.":[137],"In":[138],"order":[139],"solve":[141],"this":[142,144],"problem,":[143],"paper":[145],"introduces":[146],"attention":[148],"mechanism":[149],"proposes":[151],"BI_GRU_AT_HW_CRF_6":[153],"Experiments":[158],"show":[159],"with":[161,167],"introduction":[163],"attentional":[165],"mechanism,":[166],"change":[169],"sentence,":[171],"there":[172],"better":[175],"performance":[176],"accuracy,":[178],"training,":[179],"forecasting":[181],"data":[182],"speed.":[183]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
