Page:The World Within Wikipedia： An Ecology of Mind.pdf/14

Information 2012, 3 are not equally weighted. The magnitudes of β in Table 9 show that COALS, ESA, and WLM may be rank ordered in terms of their contribution to the overall model. However, the difference between the correlation produced by the W3C3 model in Table 8 and the correlation from the regression equation in Table 9 is extremely small (0.01), suggesting that equal weighting of the three constituent models is fairly robust.

Table 9. Regression of COALS, ESA, and WLM raw scores on MCSM similarity scores (N = 95,437).

Notes: R = 0.68, ∗ p < 0.0001.

5. Study 3: Word Association Norms

Word association norms represent a qualitatively different type of task than WordSimilarity-353 or MCSM. Typically in word association tasks, a human participant is presented with a word and asked to produce the first word that comes to mind. This production task is quite different from the raw data of MCSM, where subjects have the task constraints of listing physical, functional, or encyclopedic features and are asked to list 10 such features for a given concept. Transforming the MCSM data into a similarity matrix in Study 2 further removes the data from a stimulus-response production task and more squarely situates it with a semantic comparison task.

The relationship between semantic and associative relations has been the subject of recent discussion in the literature, , , ,. In particular, some have argued that framing word association and semantic relatedness as separate and distinct is a false dichotomy, whereas others have argued that word association and semantic feature overlap measure different kinds of information. Study 1 examined semantic relatedness quite generally; Study 2 examined similarity based on semantic feature overlap. The present study examines word association as a stimulus-response production task without the task constraints of explicitly comparing two concepts.

Perhaps the most widely known word association norms have been collected by Nelson and colleagues over several decades. The data used in the present study consists of 5019 stimulus words and their associated 72,176 responses. Each stimulus response pair is annotated by the proportion of participants who produced it, which we refer to as forward associative strength. We refer to these 72,176 triples (stimulus word/response word/forward associative strength) as NMS after the last names of its authors.

Previous work using a type of distributional model called a topic model (also known as latent Dirichlet allocation) and the TASA corpus to train it, used the NMS dataset on two tasks. The first task examined the central tendency of the model predictions via the median rank of the first five predicted responses. Thus this task first ranks the human responses for a stimulus word by their associated production probabilities and then compares these to the model’s predicted ranking. If the first five