{"id":"https://openalex.org/W2951365748","doi":"https://doi.org/10.18653/v1/p19-1532","title":"A Prism Module for Semantic Disentanglement in Name Entity Recognition","display_name":"A Prism Module for Semantic Disentanglement in Name Entity Recognition","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2951365748","doi":"https://doi.org/10.18653/v1/p19-1532","mag":"2951365748"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p19-1532","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1532","pdf_url":"https://www.aclweb.org/anthology/P19-1532.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P19-1532.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023175381","display_name":"Kun Liu","orcid":"https://orcid.org/0000-0002-5667-785X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Liu","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104910007","display_name":"Shen Li","orcid":"https://orcid.org/0000-0003-2947-3787"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shen Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications","Deeplycurious"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Deeplycurious","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077074739","display_name":"Daqi Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daqi Zheng","raw_affiliation_strings":["Deeplycurious"],"affiliations":[{"raw_affiliation_string":"Deeplycurious","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084830556","display_name":"Zhengdong Lu","orcid":"https://orcid.org/0000-0002-6418-6030"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengdong Lu","raw_affiliation_strings":["Deeplycurious"],"affiliations":[{"raw_affiliation_string":"Deeplycurious","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101707899","display_name":"Sheng Gao","orcid":"https://orcid.org/0000-0003-1591-0595"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Gao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080254938","display_name":"Li Si","orcid":"https://orcid.org/0000-0003-1028-8338"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Si Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications","Deeplycurious"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Deeplycurious","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5080254938"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.4503,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.70626345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"5358","last_page":"5362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","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/T10028","display_name":"Topic Modeling","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/T10181","display_name":"Natural Language Processing Techniques","score":0.9983999729156494,"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/T11719","display_name":"Data Quality and Management","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8518269062042236},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6478785276412964},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6336139440536499},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.6274470686912537},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.62726891040802},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.6173822283744812},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5653631687164307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5445258617401123},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4725869596004486},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.32352015376091003},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32261037826538086},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2488052248954773},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.12108638882637024},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.07696324586868286}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8518269062042236},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6478785276412964},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6336139440536499},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.6274470686912537},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.62726891040802},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.6173822283744812},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5653631687164307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5445258617401123},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4725869596004486},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32352015376091003},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32261037826538086},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2488052248954773},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.12108638882637024},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.07696324586868286},{"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},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p19-1532","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1532","pdf_url":"https://www.aclweb.org/anthology/P19-1532.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p19-1532","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p19-1532","pdf_url":"https://www.aclweb.org/anthology/P19-1532.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2951365748.pdf","grobid_xml":"https://content.openalex.org/works/W2951365748.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1484210532","https://openalex.org/W1514535095","https://openalex.org/W1614298861","https://openalex.org/W2118463056","https://openalex.org/W2119717200","https://openalex.org/W2140679639","https://openalex.org/W2147527908","https://openalex.org/W2594978815","https://openalex.org/W2597655663","https://openalex.org/W2808393080","https://openalex.org/W2950577311","https://openalex.org/W2951527505","https://openalex.org/W2952087486","https://openalex.org/W2962902328","https://openalex.org/W2962958286","https://openalex.org/W2962965405","https://openalex.org/W2963233086","https://openalex.org/W2963443335","https://openalex.org/W2963641259","https://openalex.org/W2964238855","https://openalex.org/W4285719527","https://openalex.org/W4300756893"],"related_works":["https://openalex.org/W1583765404","https://openalex.org/W4214653257","https://openalex.org/W2055438207","https://openalex.org/W2521424917","https://openalex.org/W3040203686","https://openalex.org/W4249524554","https://openalex.org/W2349021146","https://openalex.org/W35583307","https://openalex.org/W4398294854","https://openalex.org/W4381247876"],"abstract_inverted_index":{"Natural":[0],"Language":[1],"Processing":[2],"has":[3],"been":[4],"perplexed":[5],"for":[6],"many":[7],"years":[8],"by":[9],"the":[10,22,37,46,53,92,105,111,125,128,134,143],"problem":[11],"that":[12],"multiple":[13],"semantics":[14],"are":[15,58],"mixed":[16],"inside":[17],"a":[18,32,50,84,131,139],"word,":[19],"even":[20],"with":[21,61,91],"help":[23],"of":[24,40,49,113,127,145],"context.":[25],"To":[26],"solve":[27],"this":[28,88],"problem,":[29],"we":[30,81],"propose":[31],"prism":[33,54],"module":[34,89,99],"to":[35,76,86,141],"disentangle":[36],"semantic":[38,63],"aspects":[39],"words":[41,57],"and":[42,108],"reduce":[43],"noise":[44],"at":[45],"input":[47],"layer":[48],"model.":[51],"In":[52],"module,":[55],"some":[56],"selectively":[59],"replaced":[60],"task-related":[62],"aspects,":[64],"then":[65],"these":[66],"denoised":[67],"word":[68],"representations":[69],"can":[70,100],"be":[71,101],"fed":[72],"into":[73,104],"downstream":[74,93,106],"tasks":[75],"make":[77],"them":[78],"easier.":[79],"Besides,":[80],"also":[82,137],"introduce":[83],"structure":[85],"train":[87],"jointly":[90],"model":[94,107],"without":[95],"additional":[96],"data.":[97],"This":[98],"easily":[102],"integrated":[103],"significantly":[109],"improve":[110],"performance":[112],"baselines":[114],"on":[115],"named":[116],"entity":[117],"recognition":[118],"(NER)":[119],"task.":[120],"The":[121],"ablation":[122],"analysis":[123],"demonstrates":[124],"rationality":[126],"method.":[129],"As":[130],"side":[132],"effect,":[133],"proposed":[135],"method":[136],"provides":[138],"way":[140],"visualize":[142],"contribution":[144],"each":[146],"word.":[147]},"counts_by_year":[{"year":2021,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}