{"id":"https://openalex.org/W2142904027","doi":"https://doi.org/10.1109/icassp.2011.5947622","title":"Named entity recognition from Conversational Telephone Speech leveraging Word Confusion Networks for training and recognition","display_name":"Named entity recognition from Conversational Telephone Speech leveraging Word Confusion Networks for training and recognition","publication_year":2011,"publication_date":"2011-05-01","ids":{"openalex":"https://openalex.org/W2142904027","doi":"https://doi.org/10.1109/icassp.2011.5947622","mag":"2142904027"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2011.5947622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2011.5947622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5021294422","display_name":"Gakuto Kurata","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Gakuto Kurata","raw_affiliation_strings":["IBM Research Tokyo, Yamato, Kanagawa, Japan","IBM Research - Tokyo, Yamato-shi, Kanagawa 242-8502 Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Tokyo, Yamato, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210145865"]},{"raw_affiliation_string":"IBM Research - Tokyo, Yamato-shi, Kanagawa 242-8502 Japan#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028857766","display_name":"Nobuyasu Itoh","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Nobuyasu Itoh","raw_affiliation_strings":["IBM Research Tokyo, Yamato, Kanagawa, Japan","IBM Research - Tokyo, Yamato-shi, Kanagawa 242-8502 Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Tokyo, Yamato, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210145865"]},{"raw_affiliation_string":"IBM Research - Tokyo, Yamato-shi, Kanagawa 242-8502 Japan#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083574298","display_name":"Masafumi Nishimura","orcid":"https://orcid.org/0000-0001-7633-9340"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP","US"],"is_corresponding":false,"raw_author_name":"Masafumi Nishimura","raw_affiliation_strings":["IBM Research Tokyo, Yamato, Kanagawa, Japan","IBM Research - Tokyo, Yamato-shi, Kanagawa 242-8502 Japan#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research Tokyo, Yamato, Kanagawa, Japan","institution_ids":["https://openalex.org/I4210145865"]},{"raw_affiliation_string":"IBM Research - Tokyo, Yamato-shi, Kanagawa 242-8502 Japan#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088302697","display_name":"Abhinav Sethy","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhinav Sethy","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071715737","display_name":"Bhuvana Ramabhadran","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]},{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bhuvana Ramabhadran","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.#TAB#","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5572","last_page":"5575"},"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":0.9997000098228455,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.996999979019165,"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/speech-recognition","display_name":"Speech recognition","score":0.798751950263977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7724148035049438},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.7287654280662537},{"id":"https://openalex.org/keywords/word-recognition","display_name":"Word recognition","score":0.6764151453971863},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.6658921241760254},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5513418316841125},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5367336273193359},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.534834623336792},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5066211819648743},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4749581813812256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3672523498535156},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1072145402431488},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.09167999029159546},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07812529802322388}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.798751950263977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7724148035049438},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.7287654280662537},{"id":"https://openalex.org/C150856459","wikidata":"https://www.wikidata.org/wiki/Q8034367","display_name":"Word recognition","level":3,"score":0.6764151453971863},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.6658921241760254},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5513418316841125},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5367336273193359},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.534834623336792},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5066211819648743},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4749581813812256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3672523498535156},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1072145402431488},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.09167999029159546},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07812529802322388},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"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/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2011.5947622","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2011.5947622","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":21,"referenced_works":["https://openalex.org/W70399244","https://openalex.org/W117849194","https://openalex.org/W146093233","https://openalex.org/W1974515274","https://openalex.org/W1976339715","https://openalex.org/W2007261869","https://openalex.org/W2009774589","https://openalex.org/W2045993505","https://openalex.org/W2050971845","https://openalex.org/W2066680326","https://openalex.org/W2096765155","https://openalex.org/W2098318492","https://openalex.org/W2111069012","https://openalex.org/W2125234026","https://openalex.org/W2134426579","https://openalex.org/W2594610113","https://openalex.org/W6602874084","https://openalex.org/W6604739326","https://openalex.org/W6605962151","https://openalex.org/W6678457041","https://openalex.org/W6679937577"],"related_works":["https://openalex.org/W2944691285","https://openalex.org/W2401522294","https://openalex.org/W1962828410","https://openalex.org/W2253780618","https://openalex.org/W4205429340","https://openalex.org/W4232239836","https://openalex.org/W2601201456","https://openalex.org/W2079838398","https://openalex.org/W2158882055","https://openalex.org/W2136016133"],"abstract_inverted_index":{"Named":[0],"Entity":[1],"(NE)":[2],"recognition":[3,45,67,125],"from":[4,91],"the":[5,23,70,77,92,97,101,107,119,121],"results":[6,114],"of":[7,15,22,59,76,123],"Automatic":[8],"Speech":[9],"Recognition":[10],"(ASR)":[11],"is":[12,25,33,81],"challenging":[13],"because":[14],"ASR":[16,37,49],"errors.":[17],"To":[18],"detect":[19],"NEs,":[20],"one":[21],"options":[24],"to":[26,42,48,131],"use":[27],"a":[28],"statistical":[29],"NE":[30,44,64,98,124],"model":[31],"that":[32,88,116],"usually":[34],"trained":[35,96],"with":[36,100],"one-best":[38],"results.":[39],"In":[40],"order":[41],"make":[43],"more":[46],"robust":[47],"errors,":[50],"we":[51],"propose":[52],"using":[53,109,118],"Word":[54],"Confusion":[55],"Networks":[56],"(WCNs),":[57],"sequences":[58],"bundled":[60],"words,":[61],"for":[62],"both":[63],"modeling":[65],"and":[66,105],"by":[68,83,117,129],"regarding":[69],"word":[71,86],"bundles":[72,87],"as":[73],"units":[74],"instead":[75],"independent":[78],"words.":[79],"This":[80],"done":[82],"clustering":[84],"similar":[85],"may":[89],"originate":[90],"same":[93],"word.":[94],"We":[95],"models":[99],"maximum":[102],"entropy":[103],"principle":[104],"evaluated":[106],"performance":[108],"real-life":[110],"call-center":[111],"data.":[112],"The":[113],"showed":[115],"WCNs,":[120],"error":[122],"was":[126],"relatively":[127],"reduced":[128],"up":[130],"33.0%.":[132]},"counts_by_year":[{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}