Devamında bu sistemlerin faydaları ile güçlükleri ve kısıtlamaları tartışılacaktır. Ardından yazındışı metinlerin çevirisinde kullanılan bir araç olarak çeviri belleği (ÇB) sistemlerinin kullanımı üzerinde durulacaktır. Bu çalışmada, öncelikle çeviri endüstrisinin bugünkü durumu teknoloji kullanımı bakımından kısaca değerlendirilecektir. Günümüzün çeviri endüstrisinde çeviriye yardımcı kaynak ve araçların kullanılmadığı çeviri uygulamalarından söz etmek mümkün değildir. Özet Bilgi ve iletişim teknolojilerinde kaydedilen gelişmeler ve küreselleşme çevirmenlerin çalışma şeklini değiştirmiştir. Namely, if clients are made aware that they will still save time and money even if they send entire source texts some parts of which have already been translated previously, translators will not be made to translate contextless text fragments. I want to show that TM systems can eliminate the common practice of sending only text fragments to translators/translation vendors which leads to possible deteriorations in translation quality. The aim of this study is to draw attention to one particular benefit of TM systems which has not been studied with enough emphasis on until today. While the obvious reason here is to save money and time, the inability to access the entire source text, -in other words context deficiency-gives rise to various problems for translators. Today it is a common practice in the translation industry to send translation vendors/translators only the fragments of updated source texts which have not been translated before. Following a general summary of the benefits that these systems provide, their limitations and challenges are discussed. Then I concentrate on the use of translation memory (TM) system as a tool used in translating non-literary texts. In this paper, I first give a brief account of the current scene of the translation industry in terms of technology use. In today's translation industry we can no longer talk of translator's workstation that does not contain electronic resources and aids for translation. We also discuss some data we have analysed, how the system is used in commercial translation projects and how we think the data could be gathered from a wider range of cat tools while accounting for data privacy concerns.Developments in information and communication technologies as well as globalization have changed the way that translators work. ![]() In this chapter, we describe iOmegaT in more detail, including design decisions we made. To solve this problem, we have developed an instrumented version of a well-known free open-source desktop-based cat tool called OmegaT we called iOmegaT. In the long term, we believe the analysis of User Activity Data may help optimise translation technology development and translator training using various computational linguistic aids like predictive typing, interactive mt, full-sentence mt and automatic speech recognition. In the short term, these productivity analyses help buyers and translators base per-word pricing conversations for projects that use Machine Translation on hard data. Taking a similar approach, our research question is whether the analysis of this data from a Computer-aided Translation ( cat) tool used in large running translation projects can help us better understand how translators interact with machine translation ( mt). For example, data is mined from large volumes of logs that record how users interact with web-application sites like. The analysis of User Activity Data in software applications is now a common technique.
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