@incollection{DakotaKuebler2017,
  author    = {Daniel Dakota and Sandra K{\"u}bler},
  title     = {From Discourse Representation Structure to Event Semantics: A Simple Conversion?},
  series = {Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS) 1st International Workshop on AI aspects of Reasoning, Information, and Memory. Gdansk, Poland. 11-14 September 2016},
  editor    = {Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki},
  publisher = {Polish Information Processing Society},
  address   = {Warsaw},
  isbn      = {978-83-60810-90-3},
  issn      = {2300-5963},
  doi       = {10.15439/2016F440},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-61823},
  pages     = {343 -- 352},
  year      = {2017},
  abstract  = {Many applications in Natural Language Processing require a semantic analysis of sentences in terms of truth-conditional representations, often with specific desiderata in terms of which information needs to be included in the semantic analysis. However, there are only very few tools that allow such an analysis. We investigate the representations of an automatic analysis pipeline of the C\&C parser and Boxer to determine whether Boxer’s analyses in form of Discourse Representation Structure can be successfully converted into a more surface oriented event semantic representation, which will serve as input for a fusion algorithm for fusing hard and soft information. We use a data set of synthetic counter intelligence messages for our investigation. We provide a basic pipeline for conversion and subsequently discuss areas in which ambiguities and differences between the semantic representations present challenges in the conversion process.},
  language  = {en}
}