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Poster: Method for an automatic generation of a semantic-level contextual translational dictionary

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  • 1. METHOD FOR AN AUTOMATIC GENERATION OF A SEMANTIC-LEVEL CONTEXTUAL TRANSLATIONAL DICTIONARY Dmitry Kan Faculty of Applied Mathematics and Control Processes, St. Petersburg State Department of Technology of Programming, University Peterhof, Russia dmitry.kan@gmail.com Abstract Word alignment Translational dictionaryIn this paper we demonstrate the semanticfeature machine translation (MT) systemas a combination of two fundamentalapproaches, where the rule-based side is Desperate to hold onto power , Pervezsupported by the functional model of the Musharraf hasRussian language and the statistical side discarded Pakistan s constitutional framework andutilizes statistical word alignment. The declared a state of emergency .MT system relies on a semantic-level NULL ({20}) В ({}) отчаянном ({1 3 4})contextual translational dictionary as its стремлении ({2}) удержать ({}) властьkey component. We will present the ({5}) ,method for an automatic generation of the ({6}) Первез ({7}) Мушарраф ({8}) от- Parallel corpus: UMC 0.1 верг ({9 10}) 86000 pairs of sentencesdictionary where disambiguation is done конституционную ({14 15}) 1,3 million phrase pairson a semantic level. систему ({}) Пакистана ({11 12 13}) и ({16}) ~18000 resulting dictionary entries объявил ({17}) о ({18}) введении ({}) В Y1>HabU(Y1:,ПРЕД:Z1) чрезвычайного ({19 21}) <149>--->Within Computer semantics theory положения ({}) . ({22}) В Y1>Loc(Y1:,ВНУТРИ$12/313/05Thesis 1. Language is an algebraic system Table 1: Word alignment for English and Russian sentences (ПРЕД:Z1)) <146>--->at{f1, .., fn, M}, where fi is basis function and Russian English В Y1>Loc(Y1:,Oper01(#,ПРЕД:Z1))M is data structure (set of basis concepts) of NULL of <208>--->Ina natural language L. В Y1>Loc(Y1:,ПРЕД:Z1) отчаянном Desperate to holdThesis 2. Each word in a sentence S is the <224>--->Throughout стремлении to ...name of its semantic function. власть power НА Y1>Direkt(Y1:,ВЕРХ$12/141/05 (ВИН:Z1)) S  F ( f1 ( w11 ,..., w1k ),..., f n ( wn1 ,..., wnl )), , , <67>--->at Первез Pervez НА Y1>Direkt(Y1:,РОД:Z1) <100>- wij  whm , i  h, j  m -->on Мушарраф MusharrafThesis 3. Grammar links with semantics and отверг has discarded НА Y1>Direkt(Y1:,РОД:Z1) <69>--can be incorporated into semantics ->for конституционную constitutional framework ...dictionary ОБРАЗ (РОД:Z1) <2>--->a way Пакистана Pakistan ´ s ОБЩЕМИРОВОЙ A1>RelSemantic Machine Translation и and (A1:НЕЧТО$1,ПОЛНЫЙ$12/207/05 (МИР$1227)) Model объявил declared <1>--->global о a ...SMTM P  чрезвычайного state emergencyarg max  (t ,..., t )  arg max  i (tk , tl ) S s . . i 1,n i 1 m k 1,m 1 i l 2 ,mwhere 1, t k tl  L M  i (t , t )   S 2 k l 0, t k tl  L M 2 Features of Machine Translation System  dictionary entries contain semantic attributes of the Russian words  the MT system is automatically extendable through acquiring new par-  each entry represents a sample of a context extracted using statistical allel corpora and applying the method of word alignment with semantic word alignment and coded with the corresponding semantic formula; analysis of sentences on source language side