Page:The World Within Wikipedia： An Ecology of Mind.pdf/22

Information 2012, 3 regression-derived weights in all cases. This finding directly supports our stronger claim: By allowing both the language structure and category structure of Wikipedia to guide the W3C3 model, we achieved a higher correspondence to human semantic behavior than if we had used either separately. Furthermore we allowed these two dimensions to influence each other by incorporating word-word, word-concept, and concept-concept levels into the model.

Our approach is consistent with a recently proposed new model visualization for language. In this work traditional box-and-arrow models are characterized as modular, stage based, and rank ordered. In contrast, our W3C3 model does not have autonomous modules, but overlapping ones (word-word, word-concept, and concept-concept). These constituent models operate in parallel, without stages, and have no rank ordering or dominance. We differ from this new model visualization in that although our constituent models exist in multidimensional spaces (vector spaces), they do not occupy a single state space subject to dynamical state space processes.

We propose that by using vector spaces as a conceptualization, we have moved closer to unifying accounts of activation-based models and gist-based models, which are closely aligned with association and comparison-based tasks. Word association (Study 3) is a quite straightforward case of retrieval in the absence of a larger discourse context: Given a stimulus word, retrieve a response word. In contrast, semantic relatedness (Studies 1 and 2) is much more closely related to a comparison task. Intuitively, it asks the question: Given a pair of words, how does their meaning compare? We argue that distributional models by definition are much more aligned with semantic relatedness than with association. The reason is that semantic relatedness is a holistic judgment that considers many possible contexts. Distributional models are well aligned with holistic judgments because they are defined in terms of many contexts. Word association, on the other hand, can and does operate on a single dimension of a context. For example, in the NMS dataset, tumor has association with kindergarten cop. This stimulus-response pair is a clear reference to the film entitled, “Kindergarten Cop,” in which Arnold Schwarzenegger says in reference to his headache, “It’s not a tumor!” This line in this one film is quite possibly the only way in which the stimulus-response pair tumor–kindergarten cop is associated. This example illustrates that word associations need not be guided by many converging contexts but rather may be solely determined by a single context.

Study 4 in particular illustrates that gist and activation accounts are complementary views of the same underlying vector space structure. Both can have the same knowledge representation but differ in the operation performed on that structure.

Gist-like operations are inherently holistic, as in comparison tasks, and use the entire vector representation. When we applied the standard W3C3 model using gist-like measures, the correlation to backward associative strength was 0.34. In contrast, activation-like operations are inherently local, as in word association tasks, and can use only a single element of the vector. Incorporating activation into the gist-like W3C3 model by removing WLM inlink vectors increased correlation to 0.42. Further creating a completely activation-based measure using single WLM outlink vector elements increased correlation to 0.53. Since both gist-like and activation-like measures correlated positively with backward associative strength, these results explain why other studies may have evidence for either a semantic based or association based explanation of false memory,. However, rather than requiring different cognitive representations for word association or semantic comparison, we argue that word