{"id":"https://openalex.org/W2145278998","doi":"https://doi.org/10.1109/slt.2010.5700889","title":"Improved named entity extraction from conversational speech with language model adaptation","display_name":"Improved named entity extraction from conversational speech with language model adaptation","publication_year":2010,"publication_date":"2010-12-01","ids":{"openalex":"https://openalex.org/W2145278998","doi":"https://doi.org/10.1109/slt.2010.5700889","mag":"2145278998"},"language":"en","primary_location":{"id":"doi:10.1109/slt.2010.5700889","is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt.2010.5700889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Spoken Language Technology Workshop","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/A5067041166","display_name":"Man-Hung Siu","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Man-hung Siu","raw_affiliation_strings":["Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015940401","display_name":"Ted Vessenes","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ted Vessenes","raw_affiliation_strings":["Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109934165","display_name":"Ivan Bulyko","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ivan Bulyko","raw_affiliation_strings":["Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110205522","display_name":"Owen Kimball","orcid":null},"institutions":[{"id":"https://openalex.org/I1306686416","display_name":"RTX (United States)","ror":"https://ror.org/0354t7b78","country_code":"US","type":"company","lineage":["https://openalex.org/I1306686416"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Owen Kimball","raw_affiliation_strings":["Raytheon BBN Technologies, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Raytheon BBN Technologies, Cambridge, MA, USA","institution_ids":["https://openalex.org/I1306686416"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067041166"],"corresponding_institution_ids":["https://openalex.org/I1306686416"],"apc_list":null,"apc_paid":null,"fwci":0.451,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.74494369,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"14","issue":null,"first_page":"418","last_page":"423"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":1.0,"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":1.0,"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":0.9998999834060669,"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/T12031","display_name":"Speech and dialogue systems","score":0.9972000122070312,"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":0.8335914611816406},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6470304131507874},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6283053755760193},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6268267035484314},{"id":"https://openalex.org/keywords/recall-rate","display_name":"Recall rate","score":0.6082693934440613},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.59208744764328},{"id":"https://openalex.org/keywords/cascade","display_name":"Cascade","score":0.5886030197143555},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5783421397209167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5578441023826599},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.551591157913208},{"id":"https://openalex.org/keywords/named-entity","display_name":"Named entity","score":0.5228307843208313},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.45610079169273376},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4524773359298706},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.1492549180984497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8335914611816406},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6470304131507874},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6283053755760193},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6268267035484314},{"id":"https://openalex.org/C2987098735","wikidata":"https://www.wikidata.org/wiki/Q3808900","display_name":"Recall rate","level":2,"score":0.6082693934440613},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.59208744764328},{"id":"https://openalex.org/C34146451","wikidata":"https://www.wikidata.org/wiki/Q5048094","display_name":"Cascade","level":2,"score":0.5886030197143555},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5783421397209167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5578441023826599},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.551591157913208},{"id":"https://openalex.org/C2777889803","wikidata":"https://www.wikidata.org/wiki/Q25047676","display_name":"Named entity","level":2,"score":0.5228307843208313},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.45610079169273376},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4524773359298706},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.1492549180984497},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/slt.2010.5700889","is_oa":false,"landing_page_url":"https://doi.org/10.1109/slt.2010.5700889","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Spoken Language Technology Workshop","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.75,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W124954147","https://openalex.org/W130850236","https://openalex.org/W205907203","https://openalex.org/W1568558368","https://openalex.org/W2008652694","https://openalex.org/W2020073413","https://openalex.org/W2099424804","https://openalex.org/W2127836646","https://openalex.org/W2138309071","https://openalex.org/W2157143168","https://openalex.org/W2791945882","https://openalex.org/W2794655594","https://openalex.org/W6602498096","https://openalex.org/W6605282444","https://openalex.org/W6608454152","https://openalex.org/W6682982764"],"related_works":["https://openalex.org/W2347790288","https://openalex.org/W2150890698","https://openalex.org/W2401394187","https://openalex.org/W4245698648","https://openalex.org/W3133710586","https://openalex.org/W2125964738","https://openalex.org/W2098529290","https://openalex.org/W2026402306","https://openalex.org/W1566315437","https://openalex.org/W4221142855"],"abstract_inverted_index":{"Named":[0],"entity":[1,33],"(NE)":[2],"extraction":[3,15,61,132],"is":[4,96],"traditionally":[5],"applied":[6],"to":[7,16,55,87,99],"written":[8],"text.":[9],"Some":[10],"recent":[11],"works":[12],"extend":[13],"the":[14,40,57,73,79,85,90,93,113,124,150,162],"broadcast":[17],"speech":[18,51,111],"but":[19],"most":[20,97],"of":[21,92,106,155],"these":[22],"approaches":[23,54],"simply":[24],"cascade":[25],"a":[26,31,63,68,104],"speech-to-text":[27],"(STT)":[28],"engine":[29],"and":[30,52,59,134],"named":[32,47,81],"tagger":[34],"without":[35],"any":[36],"information":[37],"sharing":[38],"between":[39],"two.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"extract":[46],"entities":[48],"from":[49],"conversational":[50,110],"explore":[53],"couple":[56],"STT":[58,74,86,118],"NE":[60,100,131,136,156],"beyond":[62],"simple":[64],"cascade.":[65],"We":[66,102,121],"propose":[67],"new":[69],"approach":[70,129],"that":[71,95,123],"adapts":[72],"language":[75,125],"model":[76,126],"based":[77],"on":[78,89,108,161],"extracted":[80],"entities.":[82],"This":[83],"steers":[84],"focus":[88],"subset":[91],"vocabulary":[94],"important":[98],"extraction.":[101],"performed":[103],"number":[105],"experiments":[107],"English":[109],"in":[112],"Fisher":[114],"Corpus":[115],"with":[116,158],"different":[117],"recognition":[119],"speeds.":[120],"show":[122],"(LM)":[127],"adaptation":[128],"increases":[130],"recall":[133],"improves":[135],"performance":[137],"by":[138,145,147],"as":[139,141,143],"much":[140],"15%":[142],"measured":[144],"F-score,":[146],"significantly":[148],"improving":[149],"word":[151],"error":[152],"rate":[153],"(WER)":[154],"words,":[157],"minimal":[159],"impact":[160],"overall":[163],"WER.":[164]},"counts_by_year":[{"year":2012,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}