{ "paper_id": "O06-1022", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T08:07:39.951723Z" }, "title": "", "authors": [], "year": "", "venue": null, "identifiers": {}, "abstract": "", "pdf_parse": { "paper_id": "O06-1022", "_pdf_hash": "", "abstract": [], "body_text": [ { "text": "Stanford Parser [18] Parser Tree ", "cite_spans": [ { "start": 16, "end": 20, "text": "[18]", "ref_id": "BIBREF15" } ], "ref_spans": [], "eq_spans": [], "section": "3.1", "sec_num": null }, { "text": ") | ( max arg * E C P C c (1) S S S c E S C P E S P C ) , | ( ) | ( max arg * (2) (2) Stanford Parser S R R c c E S R C P E S R P E S P E S C P E S P C ) , , | ( ) , | ( ) | ( max arg ) , | ( ) | ( max arg * (3) (3) R P(S|E) P(R|S,E) P(C| R,S,E) P(S|E) P(R|E,S) P(C| R,S,E) (3) R ( | , , ) ( | , ) P C R S E P C S E (4) R ( | , ) P C S E C E S E C (5) E se C sc se sc P se se se sc sc sc P E C P E S C P m m ) | ( ) , , , | , , , ( ) | ( ) , | ( 2 1 2 1 (5) sc C", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "3.1", "sec_num": null }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "a w (c i , c i-1 ) w i i i i i i c c w P w c c a P c c a P ) , | ( ) , , | ( ) , | ( 1 - 1 - 1 - (10) P(a|c i , c i-1 ,w) c i c i-1 w P(w|c i , c i-1 ) c i c i-1 w cardiovascular outcomes < > c ) , | ( ular cardiovasc outcomes P i e i P(e i |c i )= 1 cardiovascular outcomes w i i i i i i e e w P w c c a P c c a P ) , | ( ) , , | ( ) , | ( 1 - 1 - 1 - (11) P(a|c i , c i-1 ) P(w|e i , e i-1 ) P(a|c i , c i-1 ,w) P(w|e i , e i-1 ) c i ,c i-1 P(a|c i , c i-1 ,w) c i ,c i-1 w P(a|c i , c i-1 ,w) Google ) , , ( ) , ,", "eq_num": ", ( ) ," } ], "section": "3.1", "sec_num": null }, { "text": "i i i i i i i i i i i i (12) N Google count(a,c i ,c i-1 ,w) w c i c i-1 \"c i wc i-1 \"( tri-gram) count(c i ,c i-1 ,w) c i c i-1 w c i c i-1", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "3.1", "sec_num": null }, { "text": "w tri-gram \"cardiovascular outcomes data\" \" \" (11) \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" \" ) | ( a P \"dietary fat composition\" \" \" \" \" \" \" \" \" \" \" \" \" \" \"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "3.1", "sec_num": null }, { "text": "C 1 ,C 2 Google", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "3.1", "sec_num": null }, { "text": "Chien [4] PAT-Tree-based", "cite_spans": [ { "start": 6, "end": 9, "text": "[4]", "ref_id": "BIBREF2" } ], "ref_spans": [], "eq_spans": [], "section": "3.1", "sec_num": null }, { "text": "Search Result", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Google dietary fat composition", "sec_num": null }, { "text": "Fluency \" \"( c 1 ) \" \"( c 2 ) \" \" Google PAT-Tree based \" \" \" \" \" \"( c 1 ) c 1 \" \" \" \"\u2026 (x 1 ) \" \" \" \" \" \"( c 2 ) c 2 \" \" \" \"\u2026 (x 2 ) \"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Phrase Translation", "sec_num": null }, { "text": "\" \" \" ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Phrase Translation", "sec_num": null }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "x 1 , x 2 c 1 c 2 (13) \" \" \" \" \" \" (x 1 ) \" \" (x 2 ) \" \" ) , ( ) , , , ( ) , ( ) , , , ( ) , , ,", "eq_num": "( 2 1" } ], "section": "Phrase Translation", "sec_num": null }, { "text": "EQUATION", "cite_spans": [], "ref_spans": [], "eq_spans": [ { "start": 0, "end": 8, "text": "EQUATION", "ref_id": "EQREF", "raw_str": "x 1 x 2 c 1 c 2 x 1 x 2 x 1 x 2 x 1 x 2 overlap x 1 x 1 x 2 1 x 2 concatenate", "eq_num": "(12)" } ], "section": "Phrase Translation", "sec_num": null } ], "back_matter": [ { "text": "In the polyp prevention trial ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "English Phrase Translation Method", "sec_num": null } ], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "The Mathematics of Statistical Machine Translation: Parameter Estimation", "authors": [ { "first": "P", "middle": [ "F" ], "last": "Brown", "suffix": "" }, { "first": "S", "middle": [ "A" ], "last": "Della Pietra", "suffix": "" }, { "first": "V", "middle": [ "J" ], "last": "Della Pietra", "suffix": "" }, { "first": "R", "middle": [ "L" ], "last": "Mercer", "suffix": "" } ], "year": 1993, "venue": "Computational Linguisics", "volume": "19", "issue": "2", "pages": "263--311", "other_ids": {}, "num": null, "urls": [], "raw_text": "P. 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Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), pp. 257-264.", "links": null } }, "ref_entries": { "FIGREF0": { "num": null, "text": "may influence remission in crohn's disease.Ding and Palmer[6] verb, adverb + verb, verb +adverb, auxiliary + verb", "uris": null, "type_str": "figure" }, "TABREF0": { "num": null, "html": null, "content": "
Brown
Translation MethodsEnglish Phraseasthma guidelines
Manual Translation
Google
(http:Brown [2]
Watanabe[20]Brown
(Chunk) Marcu Wong[13]
(Joint Probability Model)
Chiang [3]
[8][23]Och
[16] Yamada [22]
ParserParsing Tree
Yamada and Knight [21][22]
Reordering Probability Table
Insertion Probability TableTranslation Probability Table
Ding Palmer[6]
Synchronous Dependency Insertion Grammars
Grammar Induction Algorithm
Koehn[12]
Och [16] Venugopal [19]
Brown
P(T|S)P(S|T)P(T)P(T)
Foster [7]P(T|S)Och Ney [15]
P(T|S)P(S|T)P(T)
P(T|S)
(Word Sense Disambiguation)
", "type_str": "table", "text": "//www.google.com/language_tools?hl=en) IBM (http://www-306.ibm.com/software/pervasive/tech/demos/translation.shtml) SYSTRAN (http://www.systransoft.com/index.html) MojoLingo (http://text.mojolingo.com.tw/)2." } } } }