A practitioner’s view: a survey and comparison of lemmatization and morphological tagging in German and Latin
Keywords:morphological tagging, lemmatization, morphologically rich languages, NLP evaluation modelling
The challenge of POS tagging and lemmatization in morphologically rich languages is examined by comparing German and Latin. We start by defining an NLP evaluation roadmap to model the combination of tools and resources guiding our experiments. We focus on what a practitioner can expect when using state-of-the-art solutions. These solutions are then compared with old(er) methods and implementations for coarse-grained POS tagging, as well as fine-grained (morphological) POS tagging (e.g. case, number, mood). We examine to what degree recent advances in tagger development have improved accuracy – and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-of-domain evaluation. Out-of-domain evaluation is particularly pertinent because the distribution of data to be tagged will typically differ from the distribution of data used to train the tagger. Pipeline tagging is then compared with a tagging approach that acknowledges dependencies between inflectional categories. Finally, we evaluate three lemmatization techniques.
How to Cite
Copyright (c) 2019 Rüdiger Gleim, Steffen Eger, Alexander Mehler, Tolga Uslu, Wahed Hemati, Andy Lücking, Alexander Henlein, Sven Kahlsdorf, Armin Hoenen
This work is licensed under a Creative Commons Attribution 3.0 Unported License.