Name
Abstract | ||
|---|---|---|
In this paper, we report on domain clustering in the ambit of an adaptive MT architecture. A standard bottom-up hierarchical clustering algorithm has been instantiated with five different distances, which have been compared, on an MT benchmark built on 40 commercial domains, in terms of dendrograms, intrinsic and extrinsic evaluations. The main outcome is that the most expensive distance is also the only one able to allow the MT engine to guarantee good performance even with few, but highly populated clusters of domains. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | Hierarchical clustering,Data mining,Cluster (physics),Architecture,Computer science,Dendrogram,Machine translation,Artificial intelligence,Cluster analysis,Machine learning |
DocType | Volume | Citations |
Journal | abs/1612.04683 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| mauro cettolo | 1 | 539 | 55.91 |
| Mara Chinea Rios | 2 | 0 | 0.68 |
| R. Cattoni | 3 | 26 | 5.38 |