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- 253 creator steliana-ivanova.
- 253 type InProceedings.
- 253 label "POS Tagging for German: how important is the Right Context?".
- 253 sameAs 253.
- 253 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%.".
- 253 hasAuthorList authorList.
- 253 hasTopic Linguistics.
- 253 isPartOf proceedings.
- 253 keyword "Acquisition, Machine Learning".
- 253 keyword "Tagging".
- 253 title "POS Tagging for German: how important is the Right Context?".