Optimizing Spectral Learning for Parsing
Table 5: A comparison of the number of latent states for each preterminal for the German-N model, before (“b.”) running the latent state number optimization algorithm and after running it (“a.”). Note that some of the preterminals denote unary rules that were collapsed (the nonterminals in the chain are separated by |). We do not show rare preterminals with b. and a. both being 1.
['preterminal PWAT', 'freq. 64', 'b. 2', 'a. 2', 'preterminal TRUNC', 'freq. 614', 'b. 8', 'a. 1', 'preterminal PIS', 'freq. 1,628', 'b. 8', 'a. 8', 'preterminal KON', 'freq. 8,633', 'b. 8', 'a. 30']
We further inspected the behavior of the optimization algorithm for the preterminals in German-N, for which the optimal model chose (on average) a larger number of latent states. We see that in most cases, the optimization algorithm chose to decrease the number of latent states for the various preterminals, but in some cases significantly increases the number of latent states.