In the following, we show that there is a big gap between the performance achieved for some optimal parameter settings, selected through measurements performed on the test data, and the performance level when these parameters are set empirically, suggesting that more research is required to narrow this gap, and make these bootstrapping algorithms useful for practical applications.
for each word, the parameter setting (growth size G / pool size P / iterations I) leading to the highest improvement is determined.