In this paper we present further work on learning SRs from on-line corpora. The technique relays in the use of a wide-coverage noun taxonomy and a statistical measure of co-occurrence to generalize from words to semantic classes. We analyze some experimental results, detect some unsolved problems and outline possible lines of research. We claim for the need of objective evaluation measures for the SRs learning task; presenting and discussing some of them. Some variations on the basic technique, affecting the statistical association measure and thresholding, are presented and discussed. Some experimental results on these variations are reported. Some of these variations seem to improve the performance. Concluding, we summarize the future lines of research we think can lead to further improvements.