Integrating meaning into quality evaluation of machine translation

Machine translation (MT) quality is evaluated through comparisons between MT outputs and the human translations (HT). Traditionally, this evaluation relies on form related features (e.g. lexicon and syntax) and ignores the transfer of meaning reflected in HT outputs. Instead, we evaluate the quality of MT outputs through meaning related features (e.g. polarity, subjectivity) with two experiments. In the first experiment, the meaning related features are compared to human rankings individually. In the second experiment, combinations of meaning related features and other quality metrics are utilized to predict the same human rankings. The results of our experiments confirm the benefit of these features in predicting human evaluation of translation quality in addition to traditional metrics which focus mainly on form

Gösterim yoluyla öğrenen bir kapalı-devre hareket yaratma sisteminin insansı bir robotta gerçekleştirilmesi.

In this thesis the action learning and generation problem on a humanoid robot is studied. Our aim is to realize action learning, generation and recognition in one system and our inspiration source is the mirror neuron hypothesis which suggests that action learning, generation and recognition share the same neural circuitry. Dynamic Movement Primitives, an efficient action learning and generation approach, are modified in order to fulfill this aim. The system we developed (1) can learn from multiple demonstrations, (2) can generalize to different conditions, (3) generates actions in a closed-loop and online fashion and (4) can be used for online action recognition. These claims are supported by experiments and the applicability of the developed system in real world is demonstrated through implementing it on a humanoid robot.M.S. - Master of Scienc

Event Extraction from Turkish Football Web-casting Texts Using Hand-crafted Templates

In this paper, we present a domain specific information extraction approach. We use manually formed templates to extract information from unstructured documents where grammatical and syntactical errors occur frequently. We applied our approach to primarily Turkish unstructured soccer Web-casting texts. Compared to automated approaches we achieve high precision-recall rates (97% - 85%). In addition to that, unlike automated approaches we do not use part-of-speech taggers, parsers, phrase chunkers or that kind of a linguistic tool. As a result, our approach can be applied to any domain or any language without the necessity of successful linguistic tools. The drawback of our approach is the time spent on crafting the templates. We also propose the means to decrease that time