Unsupervised Clustering of Commercial Domains for Adaptive Machine Translation
Table 2: HAC instances processing time (min).
['dtrgPP()', 'dtrgPPcls/far()', 'dtrgΔ()', 'dsrc [ITALIC] CA()']
In the following sections, first the dendrograms visualize how the various instances of the algorithm perform the clustering step by step; then, intrinsic and extrinsic evaluations are provided. Each horizontal line segment indicates the merging of two clusters; the length of the vertical line segments incident to the extremes of the horizontal line segment is proportional to the distance between the merged cluster and each cluster to merge: the shorter, the more convenient the merging; viceversa, the longer, the better to avoid the merging. With this key, the dendrograms can be read as follows: dtrgPPcls(), dtrgPP() and dtrgΔ() show a growing, but anyway interesting, ability in grouping original TMs in few, compact clusters. It is a matter of fact that such ability increases with the computational cost (cf. On the contrary, dtrgPPfar() does not work at all: the well known chaining effect is here observed, likely because the low number of points to cluster. A similar behaviour is seen with dsrcCA(), for which we then omit the dendrogram.