Evaluation of Unsupervised Compositional Representations
Table 7: Clustering purity measure with coarse categories (sports, computers, religion/politics) and the original 8 categories for the Newsgroup dataset, and 6 categories for TREC. †avg and sif using si-skip.
['Model', 'Newsgroup k = 3, C=3', 'Newsgroup k = 8, C=8', 'TREC k=6, C=6']
where C={c1,...,cK} is the set of clusters, L=ℓ1,... ℓK is the set of labels, and N the total number of samples. skip-th clusters, on the other hand, did not correspond with the correct labels, underperforming binary and random features, which explains its relatively low performance in text categorization tasks. inferSent achieved higher purity than binary, random and skip-th vectors but lower than the other models, particularly with k=3.