Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Table 2. Quantitative evaluation results of hypernymy discovery from the DBLP and the LinkedIn datasets.
['Dateset Metric', 'DBLP P@100', 'DBLP P@1000', 'DBLP MaMARR', 'DBLP MiMARR', 'DBLP MaMLRR', 'DBLP MiMLRR', 'LinkedIn P@100', 'LinkedIn P@1000', 'LinkedIn MaMARR', 'LinkedIn MiMARR', 'LinkedIn MaMLRR', 'LinkedIn MiMLRR']
Overall, the HyperMine-based methods outperform all baselines under all metrics in both datasets by large margins with only one exception for P@100 in the LinkedIn dataset. Furthermore, the full HyperMine model clearly outperforms HyperMine-wo-CG in DBLP and has a competitive performance with HyperMine-wo-CG in LinkedIn dataset. Simultaneously leveraging pairwise features from multiple context granularities can introduce performance boost. We only present the metric P@1000 and omit the other metrics due to space limitations, and that similar conclusion can be reached based on other metrics. This finding corroborates our previous observation that different hypernymy pairs may be revealed from different context granularities.