Thematically ReinforcedExplicit Semantic Analysis
Table 2: Evaluation results (ordered by decreasing precision)
['[ITALIC] λ1', '[ITALIC] λ2', '[ITALIC] λ3', '[ITALIC] λ4', '[ITALIC] λ5', 'C', '# SVs', 'Precision']
The results show a significant improvement over the standard ESA version (that corresponds to λi=0 for all i. This confirms our approach. On Fig. the reader can see the precision obtained as function of the two first parameters λ1 and λ2, as well the number of support vectors used. We notice that the precision varies slightly (between 74.36% and 75.015%, that is less than 1%) as long as λ1 or λ2 are nonzero, and abruptly goes down to 65.58% when they are both zero. For nonzero values of λi the variation of precision follows no recognizable pattern. On the other hand, the number of support vectors shows a pattern: it is clearly correlated with λ1 and λ2, the highest value being 995, number of support vectors used when both λ1 and λ2 take their highest values. Since CPU time is roughly proportional to the number of support vectors, it is most interesting to take small (but nonzero) values of λi so that, at the same time, precision is high and the number of support vectors (and hence CPU time) is kept small.