FlexTag: A Highly Flexible PoS Tagging Framework
Torsten Zesch, Tobias Horsmann · 2016
We present FlexTag, a highly flexible PoS tagging framework.In contrast to monolithic implementations that can only be retrained but not adapted otherwise, FlexTag enables users to modify the feature space and the classification algorithm.We categorize existing PoS tagger implementations into one of three categories with regards to model-training capabilities and the level of access those implementations give a researcher to intrinsic details.To this categorization we add a new fourth layer characterizing our FlexTag which fully exposes the feature space and the machine learning algorithm while sustaining a high usability.With FlexTag, rapid prototyping of tagger models using different feature spaces can be easily implemented, taking a huge technical burden from the NLP researcher who is experimenting with new ideas or resources.FlexTag makes it easy to quickly develop custom-made taggers exactly fitting a research problem.We demonstrate the capabilities of FlexTag by first training a PoS tagger model with state-of-the-art performance on the common Wall-Street-Journal split using a default feature set.Furthermore, we train a social media model fitted to the Twitter domain that extends the default feature set by adding domain-specific features and incorporating knowledge from unsupervised data resources.