P-SIF: Document Embeddings Using Partition Averaging
Table 4: Multi-class classification performance on 20NewsGroups.
['[BOLD] Model', '[BOLD] Acc', '[BOLD] Prec', '[BOLD] Rec', '[BOLD] Fmes']
We observe that the dictionary learns more diverse and non-redundant topics compared to overlapping clustering (SCDV) since we require only 40 partitions rather than 60 partitions in SCDV to obtain the best performance. Simple tf-idf weighted averaging-based document representations do not show significant improvement in performance by increasing word vector dimensions. We achieve a <0.4% improvement in the accuracy when the word-vector dimensions increase from 200 to 500 on 20NewsGroup. We observe that increasing the word-vectors’ dimensions beyond 500 does not improve SIF and P-SIF ’s performances. On 20NewsGroup, we require only 20 partitions instead of 40 with Doc2VecC-initialized word vectors. This shows that better word vector representations help in learning more diverse and non-redundant partitions. We also report our results (micro-F1) on each of the 20 classes of 20NewsGroup in the Supplementary material 5. Additionally, we empirically show that our proposed embedding P-SIF outperforms the word mover distance Overall, P-SIF outperforms most methods on several datasets by a significant margin.