Towards Discourse Parsing-inspired Semantic Storytelling
This work addresses the problem of automated semantic analysis for storytelling applications, but it is incremental as it builds on existing text analytics methods.
The paper tackles the challenge of developing a robust, broad-coverage Semantic Storytelling approach by integrating discourse parsing, with preliminary experiments on a semi-automated dataset about Berlin's districts showing promising results.
Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, "Explore the Neighbourhood!", which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs