[
Table 4: Spearman correlations of the Vector Addition Baseline and the Structured Distributional Model (SDM) based on Skip-Gram on the DTFit patients subset.
['[BOLD] Dimensions', '[BOLD] Additive', '[BOLD] SDM']
Another constant finding of previous studies on thematic fit modeling was that high-dimensional, count-based vector representations perform generally better than dense word embeddings, to the point that Sayeed et al. Although the observed differences are generally small, we noticed that higher-dimensional vectors are generally better in the DTFit evaluation and, in one case, the differences reach a marginal significance (i.e., the difference between the 100-dimensional and the 400-dimensional basic Skip-Gram model is marginally significant at p<0.1). This point will also deserve future investigation, but it seems plausible that for this task embeddings benefit from higher dimensionality for encoding more information, as it has been suggested by Sayeed and colleagues. However, these advantages do not seem to be related to the injection of linguistic structure directly in the embeddings (i.e., not to the direct use of syntactic contexts for training the vectors), as bag-of-words models perform similarly to - if not better than - a syntactic-based model like C-Phrase. We leave to future research a systematic comparison with sparse count-based models to assess whether interpretable dimensions are advantageous for modeling context-sensitive thematic fit.