Name
Abstract | ||
|---|---|---|
We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies , close to established upper bounds, and outperforms several competitive baselines. |
Year | Venue | Field |
|---|---|---|
2016 | TACL | Top-down parsing language,Top-down parsing,S-attributed grammar,Computer science,Dependency grammar,Bottom-up parsing,Natural language processing,Artificial intelligence,Parser combinator,Parsing,Syntax |
DocType | Volume | Citations |
Journal | 4 | 17 |
PageRank | References | Authors |
0.97 | 22 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeljko Agic | 1 | 159 | 20.44 |
| Anders Johannsen | 2 | 150 | 12.12 |
| B. Plank | 3 | 481 | 45.29 |
| Héctor Martínez Alonso | 4 | 122 | 15.35 |
| Natalie Schluter | 5 | 45 | 9.86 |
| Anders Søgaard | 6 | 684 | 81.68 |