Abstract

This paper describes work on the grammatical tagging of a newly created Norwegian speech corpus: the first corpus of modern Norwegian speech. We use an iterative procedure to perform computer-aided manual tagging of a part of the corpus. This material is then used to train the final taggers, which are applied to the rest of the corpus. We experiment with taggers that are based on three different data-driven methods: memory-based learning, decision trees, and hidden Markov models, and find that the decision tree tagger performs best. We also test the effects of removing pauses and/or hesitations from the material before training and applying the taggers. We conclude that these attempts at cleaning up hurt the performance of the taggers, indicating that such material, rather than functioning as noise, actually contributes important information about the grammatical function of the words in their nearest context. 1

Citations