and outlinks, which are averaged together to compute the WLM metric. Table 15 presents correlations of these inlink/outlink measures separately, with the standard WLM measure, and with the corresponding W3C3 models.
By treating inlinks and outlinks separately, the correlation to backward associative strength increases
markedly. What is perhaps most interesting about the pattern of correlations in Table 15 is that the
traditional WLM metric performs worse than the two individual metrics, as though averaging somehow
cancels them out. The implication is that list words and non-present target words may share inlinks (the
same pages link to them), and they may share outlinks (they link to the same page), but they do not tend
to share both at the same time. Thus there is an implicit asymmetry to the associative relationship that
is lost if the gist-like representation considers both inlinks and outlinks. This finding is consistent with
asymmetries in human similarity judgments[1] and may also explain why LDA performs so well at
this task: It, unlike most distributional methods, is inherently asymmetric in the way it calculates gist.
Table 15. Spearman rank correlations with backward associative strength for DRM lists,
after disaggregating WLM inlink/outlink measures (N = 55).
| Model | Correlation |
| W3C3 | 0.34 |
| W3C3 (inlink) | 0.42 |
| W3C3 (outlink) | 0.42 |
| WLM | 0.24 |
| WLM (inlink) | 0.36 |
| WLM (outlink) | 0.34 |
We conducted a linear regression on ranks to evaluate the relative contributions of each constituent
model. The regression used COALS, ESA, and WLM outlink scores converted to ranks to predict the
DRM backward associative strength. In this first model COALS was not a significant predictor and
so was removed. The results of the linear regression are presented in Table 16. Tolerance analyses
were conducted to test for multicollinearity of ESA and WLM outlink by regressing each on the other.
The tolerances were both 0.98, strongly indicating a lack of multicollinearity. Consistent with previous
regressions, the fit of the model is very close, in this case identical, to the correlation of the W3C3
inlink/outlink models given in Table 15. Therefore it appears that even though COALS is not a significant
predictor in this task, it does not detract from the overall performance of the W3C3 model.
Table 16. Regression of ESA and WLM outlink ranked scores on BAS ranks (N = 55).
| Feature | B | SE(B) | β |
| ESA | 0.262 | 0.127 | 0.262 * |
| WLM (outlink) | 0.299 | 0.127 | 0.298 * |
Notes: R = 0.42, ∗p < 0.05.
- ↑ Tversky, A. Features of similarity. Psychol. Rev. 1977, 84, 327–352.
