30.2627, Diss: German; Greek, Modern; Computational Linguistics; Morphology; Text/Corpus Linguistics; Translation: Christina Valavani: ''Analysis and Processing of German Multi-word Financial Terms in Bilingual and Multilingual Applications''

[The LINGUIST List]

LINGUIST List: Vol-30-2627. Wed Jul 03 2019. ISSN: 1069 - 4875.

Subject: 30.2627, Diss: German; Greek, Modern; Computational Linguistics; Morphology; Text/Corpus Linguistics; Translation: Christina Valavani: ''Analysis and Processing of German Multi-word Financial Terms in Bilingual and Multilingual Applications''

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Date: Wed, 03 Jul 2019 17:00:14
From: Christina Valavani [cvalavani at hotmail.com]
Subject: Analysis and Processing of German Multi-word Financial Terms in Bilingual and Multilingual Applications

 
Institution: National and Kapodistrian University of Athens 
Program: Department of German Language and Literature 
Dissertation Status: Completed 
Degree Date: 2019 

Author: Christina Valavani

Dissertation Title: Analysis and Processing of German Multi-word Financial
Terms in Bilingual and Multilingual Applications 

Linguistic Field(s): Computational Linguistics
                     Morphology
                     Text/Corpus Linguistics
                     Translation

Subject Language(s): German (deu)
                     Greek, Modern (ell)


Dissertation Director(s):
Christina Alexandris
Georgios Mikros
Batsalia Freideriki

Dissertation Abstract:

The present Thesis concerns the analysis, translation and processing of German
multi-word compounds as financial and economic terms in journalistic texts and
business news. The German multi-word compounds constituting financial and
economic terms are analyzed in respect to Modern Greek and their machine
translation is evaluated with available online machine translation tools. In
particular, the GoogleTranslate machine translation tool is used, as well as
its latest updated version (with Deep Learning). Finally, an algorithm and
statistical approach is proposed, for the correct analysis and processing of
the German multi-word financial and economic terms.

The present study involves the comparison of theoretical models in German (for
example, Sternefeld, 2006, Elsen 2011 and Ralli, 2007) and in Modern Greek for
the analysis of compound words and multi-word compounds. The analysis is based
on empirical data from a large corpus of collected German financial texts and
business news available online from major German media and the German press.
According to the empirical data, sixteen (16) most commonly occurring
structures of German multi-word compounds as financial and economic terms are
determined (1).

In addition, a parallel corpus is constructed for the German financial texts
and business news and the respective equivalent terms in Modern Greek. In
addition to the parallel corpus, a separate database contains the errors and
error types for the machine translation of the German multi-word financial and
economic terms into Modern Greek. The errors and error types are determined
according to a specified set of criteria and related research in the domain of
Terminology. Five (5) main categories and various sub-categories of machine
translation errors are defined and evaluated (2). We note here that the due to
the particularities of the language pair German-Greek, most error categories
continue to persist, despite the latest developments in Machine Translation.
>From the empirical data and findings, a set of translation models is
determined (3). The constructed translation models constitute the basis for
the proposed theoretical model integrating the existing theoretical models in
German and in Modern Greek (Sternefeld, 2006, Elsen 2011 and Ralli, 2007) (4).
The theoretical model presented is connected to the proposed algorithm
analyzing German multi-word financial and economic terms. The algorithm (5),
involving the use of IBM-Models, produces re-ordered (re-ordering algorithm),
re-constructed, re-phrased equivalent financial terms and expressions in
Modern Greek, targeting to precision and correctness. The proposed algorithm
is reinforced with statistical models, for example, Bhattacharrya (2015),
which demonstrate an evident difference in output quality and efficiency in
respect to lexical-based approaches.




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