Beate Hampe: "When down is not bad, and up is not good enough" (2005)

This thoughtful paper by Beate Hampe (see also From Perception to Meaning, 2005) is a nice empirical counterweight to some of the wildly speculative claims that are thrown around in cognitive linguistics. In this particular case, the issue is whether there is an inherent and global good/bad valence to the dichotomies up/down, in/out, on/off, front/back, etc.

The Theory
This claim has apparently been made most clearly by the Polish linguistic Tomasz P. Krzeszowski. Krzeszowski himself sees the claim as a specific version of the "Invariance Principle," since it claims that up/down metaphors inherit the good/bad valencies that standing and lying down have in our preconceptual lives.

This is a neat little fairytale about the genesis of meaning, but as usual, the empirical data spoils everything. The first, obvious sign comes from looking at bad things going up:

• Unemployment is up. (bad)
• Employment is up. (good)

Faced with such examples, one would have to say something like this: up and down have an inherent emotional value, but this emotional value is not very strong itself; a strongly laden context can thus pull the words in an "unnatural" direction. However, on average or in neutral contexts, up will have a weak tendency to lean towards positive emotional valence, and down a tendency towards negative.

The Counterevidence
However, this doesn't seem to be the case. Hampe has done a medium-sized corpus study of the constructions finish off, finish up, slow down and (the rare) slow up. For each occurrence, she looked for valence clues in the immediate context and categorized the example according to how positive it was. The result was, surprisingly, that up was much more likely to be used for negative purposes than down or off.

Now, it is of course a little unfortunate that she only looked at two verbs, and that one of her particle pairs (down/off) were not antonyms. A safer strategy would be to pick two antonym verbs and two antonym particles and then combine them in a table like the following:

	in	out
give	28.0 kk	27.3 kk
take	53.5 kk	118.0 kk

The numbers in this table are the number of occurrences (in millions), estimated by Google searches for the exact phrases. This excludes, for instance, split uses like take me out (or in general verb + NP + particle). However, Hampe's study seems to have the same weakness.

Similar tables could be made for the following:

• come/go in/out
• come/leave in/out
• push/pull on/off
• break/make up/down
• break/fix up/down
• etc.

Other Sources of Evidence
Several empirical hypotheses could be tested for such data.

For instance, one test whether there is a statistical tendency for some positive particle (e.g., in) to attach to the positive set of verbs (come, make, stand, keep) compared to its negative counterpart (out). This could be done with respect to grammaticality or with respect to empirical counts. It would essentially amount to collapsing all of the tables into a single 2 × 2 table.

Another question would be whether the positive and negative contexts where distributed evenly across the two columns of any such table. In order to do so, one would have to develop a valence assessment method like Hampe's, preferably an automatic one. After having trained such a model, one could use Fisher's exact test on the contingency table consisting of positive/negative valence × positive/negative column.

The automatic valence assessment might perhaps be achieved through semi-supervised learning. We can imagine starting from a valence function v₀ defined on a small set of good and bad words:

• v₀(w) = 1 for w in some finite set G = {good, pleasant, improve, victory, truth, ...};
• v₀(w) = –1 for any w that is an antonym to a word in G;
•  v₀(w) = 0 for all other words w.

Assuming that good words co-occur with good words, this could be used to train a more fine-grained function, say, v₁₀₀₀.There are a number of problems with this assumption (it ignores rhetorical contrast effects as well as negation), but experiments would show whether it worked or not.