Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods
Table 1: Values of the GLM coefficients and their p-values for different neighborhood cosine similarity thresholds τ. β(τ)d and β(τ)r denote the coefficients for density and average frequency growth respectively for neighborhoods defined by τ. Comparing the results for the stable and relaxed control sets, we find that for the stable controls density is only significant in larger neighborhoods, but without the stability constraint both factors are significant for all neighborhood sizes.
['Neighborhood size', 'Stable control set Density', 'Stable control set Density', 'Stable control set Growth', 'Stable control set Growth', 'Relaxed control set Density', 'Relaxed control set Density', 'Relaxed control set Growth', 'Relaxed control set Growth']
We found that when comparing with the stable control set, average frequency growth rate of the neighborhood was significant for all sizes, but neighborhood density was significant at level p<0.01 only for the largest ones. We attribute this to the effect discussed in the previous section: difference in average frequency growth rate between neighbors of neologisms and stable words shrinks as we include more remote neighbors We also evaluate the significance of features for the relaxed control set without the stability constraint on 1000 neologism-control pairs. For medium-sized neighborhoods (0.4≤τ≤0.5) density variable is always significant at p<0.01, but densities of largest and smallest neighborhoods were rejected in several runs. With more variance in the control set, differences in neighborhood frequency growth rate between neologisms and controls are less prominent than in the stable setting, so density plays a more important role in prediction.