{"id":"https://openalex.org/W10455605","doi":null,"title":"Using multiple edit distances to automatically rank machine translation output","display_name":"Using multiple edit distances to automatically rank machine translation output","publication_year":2001,"publication_date":"2001-09-18","ids":{"openalex":"https://openalex.org/W10455605","mag":"10455605"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.590.6755","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.590.6755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.mt-archive.info/MTS-2001-Akiba.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5110437010","display_name":"Yasuhiro Akiba","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yasuhiro Akiba","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103546898","display_name":"Kenji Imamura","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kenji Imamura","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5033318800","display_name":"Eiichiro Sumita","orcid":"https://orcid.org/0000-0002-1028-4399","observed_orcids":["https://orcid.org/0000-0002-1028-4399"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eiichiro Sumita","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-1028-4399","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8836,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.97105653,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9955999851226807,"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":0.9955999851226807,"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":0.0008999999845400453,"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":0.0006000000284984708,"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/machine-translation-evaluation","display_name":"machine translation evaluation","score":0.8560000061988831},{"id":"https://openalex.org/keywords/edit-distance","display_name":"edit distance","score":0.9549999833106995},{"id":"https://openalex.org/keywords/decision-tree-classifier","display_name":"decision tree classification","score":0.8190000057220459},{"id":"https://openalex.org/keywords/conversational-text","display_name":"conversational text","score":0.46700000762939453}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.770452618598938},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.7340303659439087},{"id":"https://openalex.org/C203005215","wikidata":"https://www.wikidata.org/wiki/Q79798","display_name":"Machine translation","level":2,"score":0.6405078172683716},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5837220549583435},{"id":"https://openalex.org/C44359876","wikidata":"https://www.wikidata.org/wiki/Q5338467","display_name":"Edit distance","level":2,"score":0.5821435451507568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5457465648651123},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.5395300984382629},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4860451817512512},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.441049188375473},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4004889726638794},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33896592259407043},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13107869029045105},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C105580179","wikidata":"https://www.wikidata.org/wiki/Q188928","display_name":"Messenger RNA","level":3,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.590.6755","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.590.6755","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.mt-archive.info/MTS-2001-Akiba.pdf","raw_type":"text"},{"id":"mag:10455605","doi":null,"is_oa":false,"landing_page_url":"https://www.eamt.org/events/summitVIII/papers/akiba.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W80512364","https://openalex.org/W1967972613","https://openalex.org/W1970026646","https://openalex.org/W2039262760","https://openalex.org/W2070293423","https://openalex.org/W2104858828","https://openalex.org/W2125055259","https://openalex.org/W2138783964","https://openalex.org/W2463829495","https://openalex.org/W3196671539"],"related_works":["https://openalex.org/W2251763817","https://openalex.org/W2187016202","https://openalex.org/W3103313681","https://openalex.org/W2340916210","https://openalex.org/W2015807152","https://openalex.org/W2096689010","https://openalex.org/W3046034158","https://openalex.org/W2147382047","https://openalex.org/W7095481545","https://openalex.org/W2170852307"],"abstract_inverted_index":{"This":[0,84],"paper":[1,96],"addresses":[2],"the":[3,19,27,129,146,161,169,176,187],"challenging":[4],"problem":[5,28],"of":[6,21,88,124,131,157],"automatically":[7,32],"evaluating":[8],"output":[9,45,54,144],"from":[10,120],"machine":[11],"translation":[12,58],"(MT)":[13],"systems":[14],"in":[15,128,160,180,198],"order":[16],"to":[17,26,43,47,142],"support":[18],"developers":[20],"these":[22],"systems.":[23],"Conventional":[24],"approaches":[25],"include":[29],"methods":[30],"that":[31,168],"assign":[33],"a":[34,48,56,74,112,122,132,140],"rank":[35,76,81,89,113,141],"such":[36],"as":[37],"A,":[38],"B,":[39],"C,":[40],"or":[41],"D":[42],"MT":[44,143,192],"according":[46],"single":[49,61],"edit":[50,62,106],"distance":[51,63],"between":[52],"this":[53,93,95],"and":[55,183],"correct":[57],"example.":[59],"The":[60,136,149],"can":[64],"be":[65],"differently":[66],"designed,":[67],"but":[68,79],"changing":[69],"its":[70],"design":[71],"makes":[72],"assigning":[73],"certain":[75],"more":[77,173],"accurate,":[78],"another":[80],"less":[82],"accurate.":[83],"inhibits":[85],"improving":[86],"accuracy":[87],"assignment.":[90],"To":[91],"overcome":[92],"obstacle,":[94],"proposes":[97],"an":[98],"automatic":[99],"ranking":[100,178],"method":[101,138,151,171,189],"that,":[102],"by":[103,115],"using":[104,154],"multiple":[105],"distances,":[107],"encodes":[108],"machine-translated":[109],"sentences":[110],"with":[111],"assigned":[114],"humans":[116],"into":[117],"multi-dimensional":[118],"vectors":[119],"which":[121],"classifier":[123],"ranks":[125],"is":[126,152,172],"learned":[127,147],"form":[130],"decision":[133],"tree":[134],"(DT).":[135],"proposed":[137,150,170,188],"assigns":[139],"through":[145],"DT.":[148],"evaluated":[153],"transcribed":[155],"texts":[156],"real":[158],"conversations":[159],"travel":[162],"arrangement":[163],"domain.":[164],"Experimental":[165],"results":[166],"show":[167],"accurate":[174],"than":[175],"single-edit-distance-based":[177],"methods,":[179],"both":[181],"closed":[182],"open":[184],"tests.":[185],"Moreover,":[186],"could":[190],"estimate":[191],"quality":[193],"within":[194],"3":[195],"%":[196],"error":[197],"some":[199],"cases.":[200]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}