Table 5.
λ1 | 400 (12) | 600 (10) | 600 (9) | 250 (7) | 200 (6) | 100 (5) |
---|---|---|---|---|---|---|
0 | 0.76 | 0.76 | 0.76 | 0.74 | 0.69 | 0.72 |
0.1 | 0.77 | 0.77 | 0.77 | 0.75 | 0.7 | 0.72 |
0.2 | 0.78 | 0.77 | 0.77 | 0.75 | 0.71 | 0.72 |
0.3 | 0.78 | 0.78 | 0.78 | 0.76 | 0.72 | 0.73 |
0.4 | 0.79 | 0.78 | 0.79 | 0.76 | 0.73 | 0.73 |
0.5 | 0.79 | 0.79 | 0.79 | 0.76 | 0.74 | 0.73 |
0.6 | 0.79 | 0.8 | 0.8 | 0.77 | 0.75 | 0.74 |
0.7 | 0.8 | 0.8 | 0.8 | 0.77 | 0.75 | 0.74 |
0.8 | 0.8 | 0.8 | 0.8 | 0.78 | 0.76 | 0.74 |
0.9 | 0.79 | 0.79 | 0.8 | 0.77 | 0.76 | 0.75 |
1 | 0.77 | 0.77 | 0.78 | 0.76 | 0.76 | 0.74 |
Here each library is denoted as X(Y), where X is the number of fragments in the library, each of length Y. The ranking performance of a given multi-viewpoint IR model for a given library is given in terms of AUC. The multi-viewpoint model contains LDA model with weight λ1 and TF vector space model with weight 1 − λ1.