From the results shown in Figure 5, we can see that our probabilistic Co-Bootstrapping method can achieve state-of-the-art performance on all metrics: Compared with the well-known baseline Google Sets, our method can get a 42.0% improvement on P@200; Compared with the SEAL baseline, our method can get a 35.0% improvement on P@200; Compared with the WMEB method, our method can achieve a 6.2% improvement on P@100 and a 3.1% improvement on P@200.
to resolve the above two problems, this paper proposes a probabilistic Co-Bootstrapping method, which can accurately determine the expansion boundary using both the positive and the discriminant negative instances, and resolve the semantic drift problem by effectively maintaining and refining the expansion boundary during bootstrapping iterations.