Browsing by Subject "Machine Translation"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Open Access Design and implementation of a system for mapping text meaning representations to f-structures of Turkish sentences(Bilkent University, 1997) Temizsoy, Selman MuratInterlingua approach to Machine Translation (MT) aims to achieve the translation task in two independent steps. First, the meanings of source language sentences are represented in a language-independent artificial language. Then, sentences of the target language are generated from those meaning representations. Generation task in this approach is performed in three major steps among which the second step creates the syntactic structure of a sentence from its meaning representation and selects the words to be used in that sentence. This thesis focuses on the design and the implementation of a prototype system that performs this second task. The meaning representation used in this work utilizes a hierarchical world representation, ontology, to denote events and entities, and embeds semantic and pragmatic issues with special frames. The developed system is language-independent and it takes information about the target language from three knowledge resources: lexicon (word knowledge), map-rules (the relation between the meaning representation and the syntactic structure), and target language's syntactic structure representation. It performs two major tasks in processing the meaning representation: lexical selection and mapping the two representations of a sentence . The implemented system is tested on Turkish using small-sized knowledge resources developed for Turkish. The output of the system can be fed as input to a tactical generator, which is developed for Turkish, to produce the final Turkish sentences.Item Open Access Design and implementation of a verb lexicon and verb sense disambiguator for Turkish(Bilkent University, 1994) Yılmaz, OkanThe lexicon has a crucial role in all natural language processing systems and has special importance in machine translation systems. This thesis presents the design and implementation of a verb lexicon and a verb sense disambigua- tor for Turkish. The lexicon contains only verbs because verbs encode events in sentences and play the most important role in natural language processing systems, especially in parsing (syntactic analyzing) and machine translation. The verb sense disambiguator uses the information stored in the verb lexicon that we developed. The main purpose of this tool is to disambiguate senses of verbs having several meanings, some of which are idiomatic. We also present a tool implemented in Lucid Common Lisp under X-Windows for adding, accessing, modifying, and removing entries of the lexicon, and a semantic concept ontology containing semantic features of commonly used Turkish nouns.Item Open Access Learning translation templates for closely related languages(Springer, Berlin, Heidelberg, 2003) Altıntaş, Kemal; Güvenir, H. AltayMany researchers have worked on example-based machine translation and different techniques have been investigated in the area. In literature, a method of using translation templates learned from bilingual example pairs was proposed. The paper investigates the possibility of applying the same idea for close languages where word order is preserved. In addition to applying the original algorithm for example pairs, we believe that the similarities between the translated sentences may always be learned as atomic translations. Since the word order is almost always preserved, there is no need to have any previous knowledge to identify the corresponding differences. The paper concludes that applying this method for close languages may improve the performance of the system.Item Open Access Learning translation templates from examples(Elsevier, 1998) Güvenır, H. A.; Cıceklı, I.This paper proposes a mechanism for learning lexical level correspondences between two languages from a set of translated sentence pairs. The proposed mechanism is based on an analogical reasoning between two translation examples. Given two translation examples, the similar parts of the sentences in the source language must correspond to the similar parts of the sentences in the target language. Similarly, the different parts should correspond to the respective parts in the translated sentences. The correspondences between the similarities, and also differences are learned in the form of translation templates. The approach has been implemented and tested on a small training dataset and produced promising results for further investigation. (C) 1998 Elsevier Science Ltd. All rights reserved. KeywordsItem Open Access Turkish to Crimean Tatar machine translation system(Bilkent University, 2001-07) Altıntaş, KemalMachine translation has always been interesting to people since the invention of computers. Most of the research has been conducted on western languages such as English and French, and Turkish and Turkic languages have been left out of the scene. Machine translation between closely related languages is easier than between language pairs that are not related with each other. Having many parts of their grammars and vocabularies in common reduces the amount of effort needed to develop a translation system between related languages. A translation system that makes a morphological analysis supported by simpler translation rules and context dependent bilingual dictionaries would suffice most of the time. Usually a semantic analysis may not be needed. This thesis presents a machine translation system from Turkish to Crimean Tatar that uses finite state techniques for the translation process. By developing a machine translation system between Turkish and Crimean Tatar, we propose a sample model for translation between close pairs of languages. The system we developed takes a Turkish sentence, analyses all the words morphologically, translates the grammatical and context dependent structures, translates the root words and finally morphologically generates the Crimean Tatar text. Most of the time, at least one of the outputs is a true translation of the input sentence.