{"id":"https://openalex.org/W328943120","doi":"https://doi.org/10.21437/interspeech.2004-493","title":"Conditional maximum likelihood estimation for improving annotation performance of n-gram models incorporating stochastic finite state grammars","display_name":"Conditional maximum likelihood estimation for improving annotation performance of n-gram models incorporating stochastic finite state grammars","publication_year":2004,"publication_date":"2004-10-04","ids":{"openalex":"https://openalex.org/W328943120","doi":"https://doi.org/10.21437/interspeech.2004-493","mag":"328943120"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2004-493","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2004-493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2004","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/A5034451965","display_name":"Vaibhava Goel","orcid":"https://orcid.org/0000-0002-5504-3863"},"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"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vaibhava Goel","raw_affiliation_strings":["IBM, ,"],"affiliations":[{"raw_affiliation_string":"IBM, ,","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5034451965"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":1.356,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83515307,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2237","last_page":"2241"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9994000196456909,"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":0.9988999962806702,"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/rule-based-machine-translation","display_name":"Rule-based machine translation","score":0.7669992446899414},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.716083288192749},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.692240834236145},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.6678395867347717},{"id":"https://openalex.org/keywords/maximum-likelihood-sequence-estimation","display_name":"Maximum likelihood sequence estimation","score":0.5982750654220581},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5610482692718506},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5333680510520935},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.491264283657074},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4658869802951813},{"id":"https://openalex.org/keywords/n-gram","display_name":"n-gram","score":0.43175289034843445},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.4200168251991272},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.38015156984329224},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2518270015716553},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21222788095474243},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20278725028038025}],"concepts":[{"id":"https://openalex.org/C53893814","wikidata":"https://www.wikidata.org/wiki/Q7378909","display_name":"Rule-based machine translation","level":2,"score":0.7669992446899414},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.716083288192749},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.692240834236145},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.6678395867347717},{"id":"https://openalex.org/C191462741","wikidata":"https://www.wikidata.org/wiki/Q6795902","display_name":"Maximum likelihood sequence estimation","level":3,"score":0.5982750654220581},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5610482692718506},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5333680510520935},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.491264283657074},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4658869802951813},{"id":"https://openalex.org/C117884012","wikidata":"https://www.wikidata.org/wiki/Q94489","display_name":"n-gram","level":3,"score":0.43175289034843445},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.4200168251991272},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.38015156984329224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2518270015716553},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21222788095474243},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20278725028038025},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21437/interspeech.2004-493","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2004-493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2004","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W43272201","https://openalex.org/W1508165687","https://openalex.org/W2003123121","https://openalex.org/W2095734449","https://openalex.org/W2098601596","https://openalex.org/W2114561387","https://openalex.org/W2158195707"],"related_works":["https://openalex.org/W2250909759","https://openalex.org/W2532616038","https://openalex.org/W2787311093","https://openalex.org/W155946694","https://openalex.org/W1555968995","https://openalex.org/W4307474317","https://openalex.org/W2147879411","https://openalex.org/W2624072012","https://openalex.org/W2057384730","https://openalex.org/W2008468404"],"abstract_inverted_index":{"Language":[0],"models":[1,23,76,109],"that":[2,25,67,97],"combine":[3],"stochastic":[4],"grammars":[5],"and":[6,14],"N-grams":[7],"are":[8],"often":[9,42],"used":[10,29],"in":[11,33,47],"speech":[12],"recognition":[13],"language":[15,108],"understanding":[16],"systems.":[17],"One":[18],"useful":[19],"aspect":[20],"of":[21,50,74,92,106],"these":[22,75],"is":[24,84],"they":[26],"can":[27,110],"be":[28,111],"to":[30,117],"annotate":[31],"phrases":[32],"the":[34,51,71,88,103,107],"text":[35],"with":[36,98],"their":[37,78,118],"constituent":[38],"grammars;":[39],"such":[40],"annotation":[41,72,104],"plays":[43],"an":[44,58],"important":[45],"role":[46],"subsequent":[48],"processing":[49],"text.":[52],"In":[53],"this":[54],"paper":[55],"we":[56],"present":[57],"estimation":[59,83,102],"procedure,":[60],"under":[61],"a":[62],"conditional":[63,99],"maximum":[64,79,100,119],"likelihood":[65,80,101,120],"objective,":[66],"aims":[68],"at":[69],"improving":[70],"performance":[73],"over":[77,114],"estimate.":[81],"The":[82],"carried":[85],"out":[86],"using":[87],"extended":[89],"Baum-Welch":[90],"procedure":[91],"Gopalakrishnan":[93],"et.al.":[94],"We":[95],"find":[96],"accuracy":[105],"improved":[112],"by":[113],"7%":[115],"relative":[116],"estimation.":[121]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
