http://dl.gi.de/handle/20.500.12116/11109 2026-07-22T22:14:08Z http://dl.gi.de/handle/20.500.12116/11314 Challenges in Neural Computation Hammer, Barbara This contribution contains a short history of neural computation and an overview about the major learning paradigms and neural architectures used today. 2012-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11317 Connecting Question Answering and Conversational Agents Waltinger, Ulli; Breuing, Alexa; Wachsmuth, Ipke Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided by the questions. Thereby we focus on improving the conversational abilities of existing interactive interfaces by enhancing their underlying QA systems in terms of response time and correctness. As a result, we are able to introduce a method based on a tripartite contextualization. First, we present a comprehensive question classification experiment based on machine learning using two different datasets and various feature sets for the German language. Second, we propose a method for detecting the focus chunk of a given question, that is, for identifying which part of the question is fundamentally relevant to the answer and which part refers to a specification of it. Third, we investigate how to identify and label the topic of a given question by means of a human-judgment experiment. We show that the resulting contextualization method contributes to an improvement of existing question answering systems and enhances their application within interactive scenarios. 2012-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11324 Modell und Gegenstand – untrennbar miteinander verbunden Ludwig, Bernd 2012-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11311 Neural Learning of Cognitive Control Hamker, Fred H. Our goal is to develop cognitive agents based on neuroscientific evidence. The efficiency of cognitive behavior depends on its capacity to select, represent and manipulate sufficient knowledge of the environment to achieve its goals. We designed a biologically motivated model of basal ganglia and particularly the prefrontal cortex and here review its foundations of neural learning and summarize our obtained results. 2012-01-01T00:00:00Z