DFKI-LT - Towards Semantic Story Telling with Digital Curation Technologies
Towards Semantic Story Telling with Digital Curation Technologies
6 Proceedings of Natural Language Processing meets Journalism, New York, USA, print, 7/2016
We develop a system that aims at generating stories or, rather, potential story paths, based on the semantic analysis of multiple source documents (including news articles) using template-filling. The final system will be realized by additional methods, also taking specific domains and topics into account. For the processing we use NLP methods such as named entity recognition, we also use a triple store and classic document indexing modules. The analysis information is filtered, rearranged and recombined to fit the respective template. The system’s use case is to support knowledge workers (journalists, editors, curators etc.) in their tasks of processing large amounts of (incoming) documents, to identify important entities, relationships between entities and to suggest individual story paths between entities, eventually to come up with more efficient processes for content curation.