Summary of the paper
| Title | POS Tagging for German: how important is the Right Context? |
|---|---|
| Authors | Steliana Ivanova and Sandra Kuebler |
| Abstract | Part-of-Speech tagging is generally performed by Markov models, based on bigram or trigram models. While Markov models have a strong concentration on the left context of a word, many languages require the inclusion of right context for correct disambiguation. We show for German that the best results are reached by a combination of left and right context. If only left context is available, then changing the direction of analysis and going from right to left improves the results. In a version of MBT with default parameter settings, the inclusion of the right context improved POS tagging accuracy from 94.00% to 96.08%, thus corroborating our hypothesis. The version with optimized parameters reaches 96.73%. |
| Language | Single language |
| Topics | Tagging, Acquisition, Machine Learning |
Full paper |
POS Tagging for German: how important is the Right Context? |
Slides |
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| Bibtex | @InProceedings{IVANOVA08.253, author = {Steliana Ivanova and Sandra Kuebler}, title = {POS Tagging for German: how important is the Right Context?}, booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)}, year = {2008}, month = {may}, date = {28-30}, address = {Marrakech, Morocco}, editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias}, publisher = {European Language Resources Association (ELRA)}, isbn = {2-9517408-4-0}, note = {http://www.lrec-conf.org/proceedings/lrec2008/}, language = {english} } |