In this colloquium Markus Ackermann will touch on the ‘linguistic gap‘ of recent POS tagging endeavours (as perceived by C. Manning, ). Building on observations in that paper, potential paths towards more linguistically informed POS tagging are explored:
An alternative to the most widely employed ground truth for development and evaluation of POS tagging systems for English will be presented () and utilization of benefits of a DL-based representation of POS tags for a multi-tool tagging approach will be shown ().
Finally, the presenter will give an overview about work in progress with the goal to combine OWL/DL-representation of POS tags with a suitable symbolic machine learning tool (DL-Learner, ) to improve the performance of a state-of-the-art statistical POS tagger with human-interpretable post-correction rules formulated as OWL/DL-expressions.
 Christopher D. Manning. 2011. Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics? In Alexander Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing, 12th International Conference, CICLing 2011, Proceedings, Part I. Lecture Notes in Computer Science 6608, pp. 171–189.
 G.R. Sampson. 1995. English for the Computer: The SUSANNE Corpus and Analytic Scheme. Clarendon Press (Oxford University Press).
 Christian Chiarcos. 2010. Towards Robust Multi-Tool Tagging: An OWL/DL-Based Approach. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL2010.
 Jens Lehmann. 2009. DL-Learner: Learning Concepts in Description Logics. In The Journal of Machine Learning Research, Volume 10, pp. 2639-2642.