Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
Table 3: NMI results of event clustering.
['Datasets', 'TF-IDF', 'LDA', 'mSDA', 'CCG', 'WMD', 'KnowSim', 'KIES', 'KIES(T)']
Compared to other similarity measures, our KIES based k-means method achieves 6%–24% improvements in terms of NMI. We even implement a meta-path weights transfer experiment between Tencent dataset and Weibo dataset. We see that the performance of the Weibo dataset are improved more than 2% when employing the weights of the Tencent dataset to the Weibo by the KIES(T) based k-means method, but not the other way around. We believe the reason is that Tencent dataset has more applicable meta-path weights by training with manually labeled entities and keywords. Based on the learned weights and the interpretable distance metric KIES, we have achieved the best performance of semi-supervised event clustering.