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Second, we suggest a method which facilitates the definition of relevance for an analyst's context and the use of this definition to analyze new data. First, we show that predicting time to event is possible for both in-domain and cross-domain scenarios. This dissertation proposes a semi-automatic method for extracting actionable information that serves this purpose. Moreover, a significant amount of this information, if aggregated, could complement existing information networks in a non-trivial way.

Some of this aggregated information is referring to events that could or should be acted upon in the interest of e-governance, public safety, or other levels of public interest. Part of this information can be aggregated beyond the level of individual posts. Microblogs such as Twitter represent a powerful source of information.

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The value of the framework can be fully assessed after testing whether users can perform tasks better, faster and easier with a task assistant than with, for example, a manual A more definitive validation can be given if we implement the complete system and test it with human users. As a preliminary validation of our framework, we have presented a dialogue in which errors are detected and repaired, which can be constructed with the rules that we present in this thesis. Of our error handling module, only some aspects were implemented, leaving the rest of the implementation and validation for future work. Our basic framework was almost completely implemented, showing that it is indeed a valid framework. The error handling module can deal with errors such as misunderstandings and erroneous actions by the user. The basic dialogue system itself is only capable of producing and interpreting instructions that are part of the task at hand, based on principles from dialogue theory and cooperation. In order to make the system robust and generic, we present an error handling module that is separate from the basic dialogue system. We have also investigated a technique for natural language processing and generation. Then, we have constructed a framework for such a system, consisting of a basic system and an error handling module. Based on this, we have gathered a set of requirements that we believe any such system should adhere to.

We have developed this framework by investigating human-human dialogues, principles of cooperation, and dialogue systems. This allows us to simulate human-computer dialogue by representing the user as a simple BDI agent itself. Its internals are represented according to the BDI (Belief-Desire-Intention) paradigm, which enables us to consider the program and the human user in similar terms. Being an agent, it is autonomous, social, reactive and goal-directed. The framework that we present in this thesis is modeled as a BDI agent. More specifically, we have studied the generation of dialogues in which the robot iCat instructs the user to perform a task, most notably the preparation of a recipe that is chosen by the user at the beginning of the dialogue. We focus on task-oriented dialogues, since they have a relatively high level of predictability and a clear goal. In this thesis, we present a number of tactics to make the generation of human-computer dialogues more robust. Although not all errors can be solved easily, we can at least attempt to avoid total communication breakdown. Humans are unpredictable, make mistakes, vary their pronunciation, change their minds halfway through utterances or actions, and so on. Because of this, programming human-computer dialogues is also a complicated task.

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