{"id":"https://openalex.org/W2155182932","doi":"https://doi.org/10.1145/2090176.2090179","title":"Error Diagnosis of Chinese Sentences Using Inductive Learning Algorithm and Decomposition-Based Testing Mechanism","display_name":"Error Diagnosis of Chinese Sentences Using Inductive Learning Algorithm and Decomposition-Based Testing Mechanism","publication_year":2012,"publication_date":"2012-03-01","ids":{"openalex":"https://openalex.org/W2155182932","doi":"https://doi.org/10.1145/2090176.2090179","mag":"2155182932"},"language":"en","primary_location":{"id":"doi:10.1145/2090176.2090179","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2090176.2090179","pdf_url":null,"source":{"id":"https://openalex.org/S56575750","display_name":"ACM Transactions on Asian Language Information Processing","issn_l":"1530-0226","issn":["1530-0226","1558-3430"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian Language Information Processing","raw_type":"journal-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/A5110194511","display_name":"Ru-Yng Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Ru-Yng Chang","raw_affiliation_strings":["National Cheng Kung University"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103251327","display_name":"Chung\u2010Hsien Wu","orcid":"https://orcid.org/0000-0002-3947-2123"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chung-Hsien Wu","raw_affiliation_strings":["National Cheng Kung University"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062819832","display_name":"Philips Kokoh Prasetyo","orcid":null},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Philips Kokoh Prasetyo","raw_affiliation_strings":["National Cheng Kung University"],"affiliations":[{"raw_affiliation_string":"National Cheng Kung University","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110194511"],"corresponding_institution_ids":["https://openalex.org/I91807558"],"apc_list":null,"apc_paid":null,"fwci":5.1375,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.95463384,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"11","issue":"1","first_page":"1","last_page":"24"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9853000044822693,"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/T10260","display_name":"Software Engineering Research","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.7096560001373291},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6451205611228943},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.6224310994148254},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4988250732421875},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4349888265132904},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.41660264134407043},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40677475929260254},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33917751908302307}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7096560001373291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6451205611228943},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.6224310994148254},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4988250732421875},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4349888265132904},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.41660264134407043},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40677475929260254},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33917751908302307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2090176.2090179","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2090176.2090179","pdf_url":null,"source":{"id":"https://openalex.org/S56575750","display_name":"ACM Transactions on Asian Language Information Processing","issn_l":"1530-0226","issn":["1530-0226","1558-3430"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Asian Language Information Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W28279638","https://openalex.org/W46719799","https://openalex.org/W135713228","https://openalex.org/W177436765","https://openalex.org/W1496391790","https://openalex.org/W1512312253","https://openalex.org/W1518670641","https://openalex.org/W1524281572","https://openalex.org/W1565097894","https://openalex.org/W1979557360","https://openalex.org/W1981533728","https://openalex.org/W1987902506","https://openalex.org/W1996161790","https://openalex.org/W1999138184","https://openalex.org/W2001064229","https://openalex.org/W2016827714","https://openalex.org/W2042587746","https://openalex.org/W2045321313","https://openalex.org/W2056321066","https://openalex.org/W2056977501","https://openalex.org/W2061693765","https://openalex.org/W2062403165","https://openalex.org/W2083255137","https://openalex.org/W2093424574","https://openalex.org/W2105622872","https://openalex.org/W2107420359","https://openalex.org/W2109494378","https://openalex.org/W2115494134","https://openalex.org/W2123388068","https://openalex.org/W2140787270","https://openalex.org/W2147302173","https://openalex.org/W2147707543","https://openalex.org/W2161034357","https://openalex.org/W2165345215","https://openalex.org/W2242910919","https://openalex.org/W2328651900","https://openalex.org/W2408074681","https://openalex.org/W2606601345","https://openalex.org/W2893531323","https://openalex.org/W3085723970","https://openalex.org/W4229742165","https://openalex.org/W4285719527","https://openalex.org/W6601161156","https://openalex.org/W6676108733"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4290792893","https://openalex.org/W4306674287","https://openalex.org/W3107474891","https://openalex.org/W1978971213","https://openalex.org/W1567338489","https://openalex.org/W159132833","https://openalex.org/W38394648","https://openalex.org/W1517743118","https://openalex.org/W2398825887"],"abstract_inverted_index":{"This":[0,55],"study":[1],"presents":[2],"a":[3,48,80,97,121,135,139,153,163,169],"novel":[4],"approach":[5],"to":[6,67,95,137,148,182],"error":[7,30,53,69,77,178,186,200,205,232],"diagnosis":[8,31],"of":[9,32,64,127,152,190],"Chinese":[10,13,33,65],"sentences":[11,66],"for":[12,29,52,75,102,155,176,212],"as":[14],"second":[15],"language":[16],"(CSL)":[17],"learners.":[18,214],"A":[19],"penalized":[20,49],"probabilistic":[21],"First-Order":[22,43],"Inductive":[23,44],"Learning":[24,45],"(pFOIL)":[25],"algorithm":[26,37,56],"is":[27,180],"presented":[28],"sentences.":[34],"The":[35,105,141],"pFOIL":[36,81,195,220],"integrates":[38],"inductive":[39],"logic":[40],"programming":[41],"(ILP),":[42],"(FOIL),":[46],"and":[47,61,71,87,99,114,188,207,227],"log-likelihood":[50],"function":[51],"diagnosis.":[54],"considers":[57],"the":[58,111,118,128,191,194,199,204,219,223],"uncertain,":[59],"imperfect,":[60],"conflicting":[62],"characteristics":[63],"infer":[68,183],"types":[70,187,201],"produce":[72],"human-interpretable":[73],"rules":[74],"further":[76],"correction.":[78],"In":[79],"algorithm,":[82],"relation":[83,106],"pattern":[84,107,136],"background":[85,91,108,159,172],"knowledge":[86,92,109,160,173],"quantized":[88,142,156],"t":[89,143,157],"-score":[90,144,158],"are":[93,131,146],"proposed":[94,181],"characterize":[96,138,149],"sentence":[98,154,170],"then":[100,132],"used":[101,147],"likelihood":[103],"estimation.":[104],"captures":[110],"morphological,":[112],"syntactic":[113],"semantic":[115],"relations":[116,130,151],"among":[117],"words":[119],"in":[120,231],"sentence.":[122,140,192],"One":[123],"or":[124],"two":[125],"kinds":[126],"extracted":[129],"integrated":[133],"into":[134,171],"values":[145],"various":[150],"representation.":[161],"Afterwards,":[162],"decomposition-based":[164],"testing":[165],"mechanism":[166],"which":[167],"decomposes":[168],"set":[174],"needed":[175],"each":[177],"type":[179],"all":[184],"potential":[185],"causes":[189,206],"With":[193],"method,":[196],"not":[197],"only":[198],"but":[202],"also":[203],"positions":[208],"can":[209],"be":[210],"provided":[211],"CSL":[213],"Experimental":[215],"results":[216],"reveal":[217],"that":[218],"method":[221],"outperforms":[222],"C4.5,":[224],"maximum":[225],"entropy,":[226],"Naive":[228],"Bayes":[229],"classifiers":[230],"classification.":[233]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":11},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
