Abstract
How do we parse the languages for which no treebanks are available? This contribution addresses the cross-lingual viewpoint on statistical dependency parsing, in which we attempt to make use of resource-rich source language treebanks to build and adapt models for the under-resourced target languages. We outline the benefits, and indicate the drawbacks of the current major approaches. We emphasize synthetic treebanking: the automatic creation of target language treebanks by means of annotation projection and machine translation. We present competitive results in cross-lingual dependency parsing using a combination of various techniques that contribute to the overall success of the method. We further include a detailed discussion about the impact of part-of-speech label accuracy on parsing results that provide guidance in practical applications of cross-lingual methods for truly under-resourced languages.
| Original language | English |
|---|---|
| Journal | The Journal of Artificial Intelligence Research |
| Volume | 55 |
| Pages (from-to) | 209-248 |
| Number of pages | 40 |
| ISSN | 1076-9757 |
| DOIs | |
| Publication status | Published - 1 Jan 2016 |