{"id":"https://openalex.org/W2855916967","doi":null,"title":"An Empirical Study on Fine-Grained Named Entity Recognition","display_name":"An Empirical Study on Fine-Grained Named Entity Recognition","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2855916967","mag":"2855916967"},"language":"en","primary_location":{"id":"mag:2855916967","doi":null,"is_oa":false,"landing_page_url":"https://aclanthology.org/C18-1060/","pdf_url":null,"source":{"id":"https://openalex.org/S4306419219","display_name":"International Conference on Computational Linguistics","issn_l":"1525-2477","issn":["1525-2477"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Computational Linguistics","raw_type":null},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5030338233","display_name":"Khai Mai","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khai Mai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076650478","display_name":"Thai-Hoang Pham","orcid":"https://orcid.org/0000-0002-1733-6155","observed_orcids":["https://orcid.org/0000-0002-1733-6155","https://orcid.org/0009-0005-0981-2886"]},"institutions":[{"id":"https://openalex.org/I109689652","display_name":"FPT University","ror":"https://ror.org/03esj4g97","country_code":"VN","type":"education","lineage":["https://openalex.org/I109689652"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Thai-Hoang Pham","raw_affiliation_strings":["FPT University, Hanoi, Vietnam"],"raw_orcid":"https://orcid.org/0000-0002-1733-6155","affiliations":[{"raw_affiliation_string":"FPT University, Hanoi, Vietnam","institution_ids":["https://openalex.org/I109689652"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103812968","display_name":"Minh Trung Nguyen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minh Trung Nguyen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010770404","display_name":"Tuan Duc Nguyen","orcid":"https://orcid.org/0000-0001-5261-271X","observed_orcids":["https://orcid.org/0000-0001-5261-271X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuan Duc Nguyen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073503574","display_name":"Danushka Bollegala","orcid":"https://orcid.org/0000-0003-4476-7003","observed_orcids":["https://orcid.org/0000-0003-4476-7003"]},"institutions":[{"id":"https://openalex.org/I146655781","display_name":"University of Liverpool","ror":"https://ror.org/04xs57h96","country_code":"GB","type":"education","lineage":["https://openalex.org/I146655781"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Danushka Bollegala","raw_affiliation_strings":["University of Liverpool, Liverpool, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-4476-7003","affiliations":[{"raw_affiliation_string":"University of Liverpool, Liverpool, United Kingdom","institution_ids":["https://openalex.org/I146655781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049498516","display_name":"Ryohei Sasano","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryohei Sasano","raw_affiliation_strings":["Nagoya University, Nagoya, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Nagoya, Japan","institution_ids":["https://openalex.org/I60134161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112502341","display_name":"Satoshi Sekine","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1301041018","display_name":"Rakuten (Japan)","ror":"https://ror.org/0098kke80","country_code":"JP","type":"company","lineage":["https://openalex.org/I1301041018"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Sekine","raw_affiliation_strings":["Rakuten (Japan), Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rakuten (Japan), Tokyo, Japan","institution_ids":["https://openalex.org/I1301041018"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0423,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.9115598,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"711","last_page":"722"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9980000257492065,"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/T10028","display_name":"Topic Modeling","score":0.9980000257492065,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.0010000000474974513,"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":9.999999747378752e-05,"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/fine-grained-named-entity-recognition","display_name":"fine-grained named entity recognition","score":0.9990000128746033},{"id":"https://openalex.org/keywords/category-embeddings","display_name":"category embeddings","score":0.9909999966621399},{"id":"https://openalex.org/keywords/non-english-languages","display_name":"non-English languages","score":0.6340000033378601},{"id":"https://openalex.org/keywords/training-set-size","display_name":"training data size","score":0.6039999723434448}],"concepts":[{"id":"https://openalex.org/C2779135771","wikidata":"https://www.wikidata.org/wiki/Q403574","display_name":"Named-entity recognition","level":3,"score":0.8904831409454346},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8349631428718567},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6760035753250122},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6405269503593445},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.5081398487091064},{"id":"https://openalex.org/C96711827","wikidata":"https://www.wikidata.org/wiki/Q17012245","display_name":"Entity linking","level":3,"score":0.4436340928077698},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43605679273605347},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43580326437950134},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4340170919895172},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.4144664406776428},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08158394694328308},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.06107419729232788},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"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/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","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}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2855916967","doi":null,"is_oa":false,"landing_page_url":"https://aclanthology.org/C18-1060/","pdf_url":null,"source":{"id":"https://openalex.org/S4306419219","display_name":"International Conference on Computational Linguistics","issn_l":"1525-2477","issn":["1525-2477"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Computational Linguistics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W61015605","https://openalex.org/W178774507","https://openalex.org/W638568100","https://openalex.org/W1951325712","https://openalex.org/W1991903527","https://openalex.org/W2022166150","https://openalex.org/W2094728533","https://openalex.org/W2123512824","https://openalex.org/W2132096166","https://openalex.org/W2141099517","https://openalex.org/W2153848201","https://openalex.org/W2179519966","https://openalex.org/W2251264718","https://openalex.org/W2296283641","https://openalex.org/W2335957011","https://openalex.org/W2398591267","https://openalex.org/W2406945108","https://openalex.org/W2579480569","https://openalex.org/W2740215900","https://openalex.org/W2756748969","https://openalex.org/W2962902328"],"related_works":["https://openalex.org/W2756748969","https://openalex.org/W4385571149","https://openalex.org/W4214886308","https://openalex.org/W3201535928","https://openalex.org/W4409548838","https://openalex.org/W3129710953","https://openalex.org/W3035313396","https://openalex.org/W3214162883","https://openalex.org/W2250233410","https://openalex.org/W7035928327"],"abstract_inverted_index":{"Named":[0],"entity":[1,37,76],"recognition":[2],"(NER)":[3],"has":[4,130],"attracted":[5],"a":[6,55,127,131,143],"substantial":[7],"amount":[8],"of":[9,35,74,81,91,107,134],"research.":[10],"Recently,":[11],"several":[12],"neural":[13,148],"network-based":[14,149],"models":[15,93],"have":[16],"been":[17],"proposed":[18,169],"and":[19,78,96,98,103,158,161],"achieved":[20],"high":[21],"performance.":[22],"However,":[23],"there":[24,53],"is":[25,47,54,58],"little":[26],"research":[27],"on":[28,69],"fine-grained":[29],"NER":[30],"(FG-NER),":[31],"in":[32],"which":[33],"hundreds":[34],"named":[36,75],"categories":[38],"must":[39],"be":[40],"recognized,":[41],"especially":[42],"for":[43,94,111,117,125],"non-English":[44],"languages.":[45],"It":[46],"still":[48],"an":[49,88],"open":[50],"question":[51],"whether":[52],"model":[56,66],"that":[57,100,129,167],"robust":[59],"across":[60],"various":[61],"settings":[62],"or":[63],"the":[64,70,72,79,108,147,155,168],"proper":[65],"varies":[67],"depending":[68],"language,":[71],"number":[73,133],"categories,":[77],"size":[80],"training":[82],"datasets.":[83],"This":[84],"paper":[85],"first":[86],"presents":[87],"empirical":[89],"comparison":[90],"FG-NER":[92,119,151,173],"English":[95,112,118],"Japanese":[97,150,172],"demonstrates":[99],"LSTM+CNN+CRF":[101],"(Ma":[102],"Hovy,":[104],"2016),":[105],"one":[106],"state-of-the-art":[109],"methods":[110],"NER,":[113],"also":[114],"works":[115],"well":[116,124],"but":[120],"does":[121],"not":[122],"work":[123],"Japanese,":[126],"language":[128],"large":[132],"character":[135],"types.":[136],"To":[137],"tackle":[138],"this":[139],"problem,":[140],"we":[141],"propose":[142],"method":[144,170],"to":[145,177],"improve":[146],"performance":[152],"by":[153],"removing":[154],"CNN":[156],"layer":[157],"utilizing":[159],"dictionary":[160],"category":[162],"embeddings.":[163],"Experiment":[164],"results":[165],"show":[166],"improves":[171],"F-score":[174],"from":[175],"66.76%":[176],"75.18%.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2018-07-19T00:00:00"}
