Semantic Annotation for the Digital Humanities - Using Markov Logic for Annotation Consistency Control


Abstract


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 per- formance 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 con- sistency 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 inter- action 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.

Keywords


treebank; annotation; digital humanities; semantic annotation; Markov Logic

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