Name
Abstract | ||
|---|---|---|
We develop dependency parsers for Arabic, English, Chinese, and Czech using Bayes Point Machines, a training algorithm which is as easy to implement as the perceptron yet competitive with large margin methods. We achieve results comparable to state-of-the-art in English and Czech, and report the first directed dependency parsing accuracies for Arabic and Chinese. Given the multilingual nature of our experiments, we discuss some issues regarding the comparison of dependency parsers for different languages. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.3115/1220835.1220856 | HLT-NAACL |
Keywords | Field | DocType |
multilingual nature,large margin method,dependency parsers,bayes point machines,training algorithm,multilingual dependency,different language,dependency parsing | Czech,Arabic,Computer science,Dependency grammar,Natural language processing,Artificial intelligence,Parsing,Perceptron,Bayes' theorem | Conference |
Citations | PageRank | References |
15 | 0.79 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Corston-Oliver | 1 | 349 | 25.25 |
| Anthony Aue | 2 | 290 | 16.87 |
| Kevin Duh | 3 | 819 | 72.94 |
| Eric Ringger | 4 | 328 | 21.57 |