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 dictionary
In this paper we demonstrate the semantic
feature machine translation (MT) system
as a combination of two fundamental
approaches, where the rule-based side is Desperate to hold onto power , Pervez
supported by the functional model of the Musharraf has
Russian language and the statistical side discarded Pakistan ' s constitutional
framework and
utilizes 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 ({}) власть
key 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 sentences
dictionary where disambiguation is done конституционную ({14 15}) 1,3 million phrase pairs
on 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$12/313/05
Thesis 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>--->In
a natural language L. В Y1>Loc(Y1:,ПРЕД:Z1)
отчаянном Desperate to hold
Thesis 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 Musharraf
Thesis 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>Rel
Semantic Machine Translation и and (A1:НЕЧТО$1,ПОЛНЫЙ$12/207/05
(МИР$1227))
Model объявил declared
<1>--->global
о a ...
SMTM P 
чрезвычайного state emergency
arg max  (t ,..., t )  arg max  i (tk , tl )
S s
. .
i 1,n i 1 m k 1,m 1 i
l 2 ,m
where
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