Semantic Annotation for the Digital Humanities

This contribution investigates novel techniques for error detection in automatic semantic annotations, as an attempt to reconcile error-prone NLP processing with high quality standards required for empirical research in Digital Humanities. We demonstrate the state-of-the-art performance of semantic NLP systems on a corpus of ritual texts and report performance gains we obtain using domain adaptation techniques. Our main contribution is to explore new techniques for annotation consistency control, as an attempt to reconcile error-prone NLP processing with high quality requirements. The novelty of our approach lies in its attempt to leverage multi-level semantic annotations by defining interaction constraints between local word-level semantic annotations and global discourse-level annotations. These constraints are defined using Markov Logic Networks, a logical formalism for statistical relational inference that allows for violable constraints. We report first results.