Lisa Madlberger

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Twitter data is increasingly used to make predictions about real-world events. However recently, several studies directly or indirectly questioned proposed Twitter prediction procedures. In this paper, we conduct a literature review to investigate the research processes adopted by previous Twitter prediction studies in detail. We first identify the actors(More)
Many international corporations have globally distributed supply chains exposing their operations to various local risks, e.g., natural disasters. To facilitate assessment of these risks, corporations have to identify geographic locations of their suppliers. However, automated identification of supplier locations is problematic for areas where geocoding of(More)
The absence of manually annotated training data presents an obstacle for the development of machine-learning based NLP tools in Indonesia. Existing annotation tools lack a mobile-friendly interface which is a problem in Indonesia where most users access the internet using their smartphone. In this paper, we propose the first mobile collaborative data(More)
Nowadays, the term sustainability gains more and more importance in corporate risk management and decision making. Corporate Sustainability Information Systems should support companies to analyse sustainability risks and provide relevant data in a practical manner. A major challenge in the domain of sustainability is to integrate environmental, social and(More)
In recent years, enterprises and emergency response teams have started to use user-generated content to monitor crises, events and trends. Especially in critical situations, decision makers must, above all, quickly assess huge amounts of data. Effective geographical visualization and aggregation of collected data is an important prerequisite to enable(More)
Semantic role labeling (SRL) is a task to assign semantic role labels to sentence elements. This paper describes the initial development of an Indonesian semantic role labeling system and its application to extract event information from Tweets. We compare two feature types when designing the SRL systems: Word-to-Word and Phrase-to-Phrase. Our experiments(More)
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