Browsing by Subject "Translation (languages)"

Now showing 1 - 5 of 5

Open Access
Haber videolarında nesne tanıma ve otomatik etiketleme
(IEEE, 2006-04) Baştan, Muhammet; Duygulu, Pınar
We propose a new approach to object recognition problem motivated by the availability of large annotated image and video collections. Similar to translation from one language to another, this approach considers the object recognition problem as the translation of visual elements to words. The visual elements represented in feature space are first categorized into a finite set of blobs. Then, the correspondences between the blobs and the words are learned using a method adapted from Statistical Machine Translation. Finally, the correspondences, in the form of a probability table, are used to predict words for particular image regions (region naming), for entire images (auto-annotation), or to associate the automatically generated speech transcript text with the correct video frames (video alignment). Experimental results are presented on TRECVID 2004 data set, which consists of about 150 hours of news videos associated with manual annotations and speech transcript text. © 2006 IEEE.
Open Access
Inducing translation templates with type constraints
(Springer, 2005) Çiçekli, İlyas
This paper presents a generalization technique that induces translation templates from a given set of translation examples by replacing differing parts in the examples with typed variables. Since the type of each variable is inferred during the learning process, each induced template is also associated with a set of type constraints. The type constraints that are associated with a translation template restrict the usage of the translation template in certain contexts in order to avoid some of the wrong translations. The types of variables are induced using type lattices designed for both the source and target languages. The proposed generalization technique has been implemented as a part of an example-based machine translation system.
Open Access
Learning translation templates for closely related languages
(Springer, Berlin, Heidelberg, 2003) Altıntaş, Kemal; Güvenir, H. Altay
Many 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.
Open Access
An ontology-based approach to parsing Turkish sentences
(Springer, 1998-10) Temizsoy, Murat; Çiçekli, ilyas
The main problem with natural language analysis is the ambiguity found in various levels of linguistic information. Syntactic analysis with word senses is frequently not enough to resolve all ambiguities found in a sentence. Although natural languages are highly connected to the real world knowledge, most of the parsing architectures do not make use of it effectively In this paper, a new methodology is proposed for analyzing Turkish sentences which is heavily based on the constraints in the ontology. The methodology also makes use of morphological marks of Turkish which generally denote semantic properties. Analysis aims to find the propositional structure of the input utterance without constructing a deep syntactic tree, instead it utilizes a weak interaction between syntax and semantics. The architecture constructs a specific meaning representation on top of the analyzed propositional structure. © Springer-Verlag Berlin Heidelberg 1998.
Open Access
Ordering translation templates by assigning confidence factors
(Springer Verlag, 1998) Öz, Zeynep; Çiçekli, İlyas
TTL (Translation Template Learner) algorithm learns lexical level correspondences between two translation examples by using analogical reasoning. The sentences used as translation examples have similar and different parts in the source language which must correspond to the similar and different parts in the target language. Therefore these correspondences are learned as translation templates. The learned translation templates are used in the translation of other sentences. However, we need to assign confidence factors to these translation templates to order translation results with respect to previously assigned confidence factors. This paper proposes a method for assigning confidence factors to translation templates learned by the TTL algorithm. Training data is used for collecting statistical information that will be used in confidence factor assignment process. In this process, each template is assigned a confidence factor according to the statistical information obtained from training data. Furthermore, some template combinations are also assigned confidence factors in order to eliminate certain combinations resulting bad translation. © Springer-Verlag Berlin Heidelberg 1998.