{"id":"https://openalex.org/W2072221528","doi":"https://doi.org/10.3115/1117794.1117805","title":"Detection of language (model) errors","display_name":"Detection of language (model) errors","publication_year":2000,"publication_date":"2000-01-01","ids":{"openalex":"https://openalex.org/W2072221528","doi":"https://doi.org/10.3115/1117794.1117805","mag":"2072221528"},"language":"en","primary_location":{"id":"doi:10.3115/1117794.1117805","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1117794.1117805","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1117794.1117805","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th 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://dl.acm.org/doi/pdf/10.3115/1117794.1117805","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5112412076","display_name":"K.-Y. Hung","orcid":null},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"K. Y. Hung","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong","#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029313981","display_name":"Robert W. P. Luk","orcid":"https://orcid.org/0000-0002-9310-8867"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"R. W. P. Luk","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong","#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105759393","display_name":"Wing W. Y. Ng","orcid":"https://orcid.org/0000-0003-0783-3585"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"D. Yeung","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong","#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043016512","display_name":"Fu-Lai Chung","orcid":"https://orcid.org/0000-0001-5294-8168"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"K. F. L. Chung","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong","#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109184956","display_name":"Wenhao Shu","orcid":"https://orcid.org/0000-0003-2422-6760"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"W. Shu","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong","#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#"],"affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong","institution_ids":["https://openalex.org/I14243506"]},{"raw_affiliation_string":"#N##TAB##TAB##TAB##TAB# Hong Kong Polytechnic University, Hong Kong#N##TAB##TAB##TAB#","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5112412076"],"corresponding_institution_ids":["https://openalex.org/I14243506"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.12985089,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"87","last_page":"94"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","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/T10181","display_name":"Natural Language Processing Techniques","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/T10028","display_name":"Topic Modeling","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.9939000010490417,"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/bigram","display_name":"Bigram","score":0.8686705827713013},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7988782525062561},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6981656551361084},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6651138663291931},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5958499312400818},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5594757199287415},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.5084115862846375},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5025582313537598},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4805172383785248},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.4803772568702698},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.47261762619018555},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.45739778876304626},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44274213910102844},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4298509657382965},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.14401158690452576}],"concepts":[{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.8686705827713013},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7988782525062561},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6981656551361084},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6651138663291931},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5958499312400818},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5594757199287415},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.5084115862846375},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5025582313537598},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4805172383785248},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.4803772568702698},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.47261762619018555},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.45739778876304626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44274213910102844},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4298509657382965},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14401158690452576},{"id":"https://openalex.org/C137546455","wikidata":"https://www.wikidata.org/wiki/Q3213474","display_name":"Trigram","level":2,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3115/1117794.1117805","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1117794.1117805","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1117794.1117805","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics -","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.13.476","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://acl.ldc.upenn.edu/W/W00/W00-1311.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.818.6933","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.818.6933","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.jhu.edu/%7Eyarowsky/acl2000/sigdat/hung.pdf","raw_type":"text"},{"id":"pmh:oai:ira.lib.polyu.edu.hk:10397/57904","is_oa":false,"landing_page_url":"http://hdl.handle.net/10397/57904","pdf_url":null,"source":{"id":"https://openalex.org/S4306400205","display_name":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I14243506","host_organization_name":"Hong Kong Polytechnic University","host_organization_lineage":["https://openalex.org/I14243506"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"}],"best_oa_location":{"id":"doi:10.3115/1117794.1117805","is_oa":true,"landing_page_url":"https://doi.org/10.3115/1117794.1117805","pdf_url":"https://dl.acm.org/doi/pdf/10.3115/1117794.1117805","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics -","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4000000059604645,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322598","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2072221528.pdf","grobid_xml":"https://content.openalex.org/works/W2072221528.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W41384227","https://openalex.org/W1531990095","https://openalex.org/W1553385386","https://openalex.org/W1590952807","https://openalex.org/W1845723635","https://openalex.org/W1850668662","https://openalex.org/W1966812932","https://openalex.org/W1970026646","https://openalex.org/W1978337264","https://openalex.org/W1984675336","https://openalex.org/W1994920552","https://openalex.org/W2013196554","https://openalex.org/W2039307316","https://openalex.org/W2064723603","https://openalex.org/W2096077649","https://openalex.org/W2108820170","https://openalex.org/W2110145858","https://openalex.org/W2110903505","https://openalex.org/W2121227244","https://openalex.org/W2124045530","https://openalex.org/W2125055259","https://openalex.org/W2128236863","https://openalex.org/W2131528687","https://openalex.org/W2156909104","https://openalex.org/W2160202716","https://openalex.org/W2169518243","https://openalex.org/W2441154163","https://openalex.org/W4234231991","https://openalex.org/W4301968361","https://openalex.org/W6642850889","https://openalex.org/W6653769920","https://openalex.org/W6674928664","https://openalex.org/W6678277124","https://openalex.org/W6678449394","https://openalex.org/W6683463268"],"related_works":["https://openalex.org/W2105076537","https://openalex.org/W1700330385","https://openalex.org/W2041167939","https://openalex.org/W2002221802","https://openalex.org/W2182912008","https://openalex.org/W2250909759","https://openalex.org/W2562995433","https://openalex.org/W2131111393","https://openalex.org/W2028371633","https://openalex.org/W2020757772"],"abstract_inverted_index":{"The":[0,110],"bigram":[1],"language":[2,8,20,57],"models":[3],"are":[4],"popular,":[5],"in":[6,11,26,39],"much":[7],"processing":[9],"applications,":[10],"both":[12,100],"Indo-European":[13],"and":[14,51,69,96,102,119],"Asian":[15],"languages.":[16],"However,":[17],"when":[18],"the":[19,30,78,85,115,127],"model":[21,58],"for":[22],"Chinese":[23],"is":[24,32,87,126],"applied":[25],"a":[27],"novel":[28],"domain,":[29],"accuracy":[31],"reduced":[33],"significantly,":[34],"from":[35],"96%":[36],"to":[37,55],"78%":[38],"our":[40,73],"evaluation.":[41],"We":[42,60],"apply":[43],"pattern":[44],"recognition":[45],"techniques":[46],"(i.e.":[47],"Bayesian,":[48],"decision":[49,111],"tree":[50,112],"neural":[52],"network":[53,91,104],"classifiers)":[54],"discover":[56],"errors.":[59],"have":[61,105],"examined":[62],"2":[63],"general":[64],"types":[65],"of":[66,82],"features:":[67],"model-based":[68],"language-specific":[70],"features.":[71],"In":[72],"evaluation,":[74],"Bayesian":[75,101],"classifiers":[76],"produce":[77],"best":[79,116],"recall":[80,94,125],"performance":[81],"80%":[83],"but":[84,99,123],"precision":[86,97,117],"low":[88,106],"(60%).":[89],"Neural":[90,103],"produced":[92,114],"good":[93],"(75%)":[95],"(80%)":[98],"skip":[107,120],"ratio":[108,121],"(65%).":[109],"classifier":[113],"(81%)":[118],"(76%)":[122],"its":[124],"lowest":[128],"(73%).":[129]},"counts_by_year":[],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
