Comparing Data-Driven Learning Algorithms for PoS Tagging of Swedish
2001 (English)In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2001), 2001Conference paper (Refereed)
The aim of this study is a systematic evaluation and comparison of four state-of-the-art data-driven 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 effects of tag set and the effects 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 differences between the approaches due to the different linguistic information built into the systems.
Place, publisher, year, edition, pages
Language Technology (Computational Linguistics)
Research subject Computational Linguistics
IdentifiersURN: urn:nbn:se:uu:diva-19650OAI: oai:DiVA.org:uu-19650DiVA: diva2:47422
Empirical Methods in Natural Language Processing