Cross-Language Dependency Parsing Using Part-of-Speech Patterns

摘要

The presented paper describes a simple instance-based learning method for dependency parsing, which is based solely on the part-of-speech n-grams extracted from training data. The presented method is not dependent on any lexical features (i.e. words or lemmas) or other morphological categories so model trained on one language can be directly applied to another similar language with harmonized tagset of coarse-grained part-of-speech categories. Using the instance-based learning allows us to directly evaluate predictive power of part-of-speech patterns on evaluation data from Czech and Slovak treebanks.