Abstract
This paper shows how the best data-driven dependency parsers available today [1] can be improved by learning from unlabeled data. We focus on German and Swedish and show that labeled attachment scores improve by 1.5%-2.5%. Error analysis shows that improvements are primarily due to better recovery of long distance dependencies.
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Søgaard, A., Rishøj, C. (2010). The Effect of Semi-supervised Learning on Parsing Long Distance Dependencies in German and Swedish. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds) Advances in Natural Language Processing. NLP 2010. Lecture Notes in Computer Science(), vol 6233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14770-8_44
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DOI: https://doi.org/10.1007/978-3-642-14770-8_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14769-2
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