The thesis describes methods for automatic creation of illustrations
of natural-language text.
The main focus of the work is
to convert texts that describe
sequences of events in a
physical world into animated images.
This is what we call text-to-scene conversion.
The first part of the thesis describes Carsim, a system that automatically
illustrates traffic accident newspaper reports written in
Swedish. This system is the first text-to-scene conversion system for
non-invented texts.
The second part of the thesis focuses on methods to generalize the NLP
components of Carsim to make the system more easily portable to new
domains of
application. Specifically, we develop methods to sidestep the
scarcity of annotated data, needed for training and testing of NLP
methods. We present a method to annotate the Swedish side of a
parallel corpus with shallow semantic information in the FrameNet
standard. This corpus is then used to train a semantic role labeler
for Swedish text.