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Cognitive Semantics : Meaning and Cognition

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188 JORDAN ZLATEV<br />

respond by giving the red car, in analogy to the red ball <strong>and</strong> the blue car. But,<br />

then again, the child may make a mistake, <strong>and</strong> the parent will have to correct<br />

it: “No, not the blue car, the red car” — <strong>and</strong> the process of training will<br />

continue. Once the child has mastered the distinctions in this game, it can<br />

generalize <strong>and</strong> use this ability in other games: it will be able to draw red suns<br />

<strong>and</strong> blue moons, but not because it has formed some “context-independent”<br />

concept of REDNESS, but because new uses constantly draw on old ones, in the<br />

accumulative ability to use language appropriately in ever new contexts.<br />

A number of experiments with the extended version of Regier’s system<br />

(cf. Figure 4) were performed in order to lend some empirical support for this<br />

view <strong>and</strong> against Chomsky’s “anti-generalization” position. The system was<br />

trained on some, but not all verb-preposition pairs, e.g., on the pairs “be<br />

over”, “be under”, “go over”, “go under” <strong>and</strong> “fly over”, but not on the sixth<br />

remaining pair “fly under”. 4 The hypothesis was that from using “under” in<br />

two other contexts <strong>and</strong> “fly” in one other, the system would generalize <strong>and</strong><br />

when presented with a novel kind of situation, a trajector flying under a<br />

l<strong>and</strong>mark the system would (by analogy) “light up” the two appropriate nodes:<br />

“fly” <strong>and</strong> “under”.<br />

The first results were very encouraging. When presenting a set of 30<br />

novel fly under-situations, we found that only 2 were classified as “go under”,<br />

all others were correctly classified as “fly under”. However, when testing the<br />

ability of the net to generalize to other novel word-pairs, e.g., “go over” (after<br />

training on all the rest), we found that the net performed less well — there<br />

were many overgeneralizations (“false alarms”) <strong>and</strong> even worse, many undergeneralizations<br />

(“misses”). A natural conclusion was that the environmental<br />

“evidence” was insufficient, i.e. the system needed to “see” a larger<br />

number of different over <strong>and</strong> under situations before it could extract a more<br />

adequate notion of the semantic contribution of the individual words. Therefore<br />

I gradually extended the training to include situations that can be described<br />

with other verbs <strong>and</strong> prepositions. The most extensive training session<br />

was performed for the verbs: “be”, “move”, “pass” <strong>and</strong> “fall” <strong>and</strong> the<br />

prepositions “over”, “under”, “in”, “through” <strong>and</strong> “on”. The choice of the<br />

words was motivated by following the naming of example “movies” by a<br />

native speaker of English. However, the results were negative: not only did<br />

extending the context of use not make the generalization better, it seemed to<br />

make it worse. In most cases a novel situation would be classified not with a<br />

novel expression but with the non-novel expression used to label some similar

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