Comparing Data-Driven Learning Algorithms for PoS Tagging of Swedish.
Beáta Megyesi · 2001
The aim of this study is a systematic evaluation and comparison of four state-of-the-art datadriven learning algorithms applied to part of speech tagging of Swedish. The algorithms included in this study are Hidden Markov Model, Maximum Entropy, Memory-Based Learning, and Transformation-Based Learning. The systems are evaluated from several aspects. Both the eects of tag set and the eects of the size of training data are examined. The accuracy is calculated as well as the error rate for known and unknown tokens. The results show dierences between the approaches due to the different linguistic information built into the systems.