I really liked seeing the technique imports from the NLP world (using embeddings, using classifiers), in the service of psychologically-motivated theories of adjective ordering. Yes! Good tools are wonderful.
I also love seeing this kind of direct, head-to-head competition between well-defined theories, grounding in a well-defined empirical dataset (complete with separate evaluation set), careful qualitative analysis, and discussion of why certain theories might work out better than others. Hurrah for good science!
Other thoughts:
(1) Integration cost vs information gain (subtle differences): Information gain seems really similar to the integration cost idea, where the size of the set of nouns an adjective could modify is the main thing (as the text notes). Both approaches care about making that entropy gain smaller the further the adjective is away from the noun (since that’s less cognitively-taxing to deal with). The difference (if I’m reading this correctly) is that information gain cares about the set size of the nouns the adjective can’t modify too, and uses that in its entropy calculation.
(2) I really appreciate the two-pronged explanation of (a) the more generally semantic factors (because of improved performance when using the semantic clusters for subjectivity and information gain), and (b) the collocation factor over specific lexical items (because of the improved performance on individual wordforms for PMI). But it’s not clear to me how much information gain is adding above and beyond subjectivity on the semantic factor side. I appreciate the item-based zoom in Table 3, which shows the items that information gain does better on...but it seems like these are wordform-based, not based on general semantic properties. So, the argument that information gain is an important semantic factor is a little tricky for me to follow.
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