Abstract
Table 6.3: A qualitative comparison between using a window (BROWN) and not having a window, equivalent to running clustering over the entire corpus (BROWN_NW). Quality is measured both directly on clusters through final average mutual information (the 5th column) and on the resulting class-based language models. The test corpus TT is used to measure language model characteristics such as class prediction accuracy (3rd column) and perplexity (4th column). Results for the T50 corpus are omitted as the experiments on such a large corpus take too long to complete.
['[BOLD] Corpus', '[BOLD] Language model', '[BOLD] CPA (%)', '[BOLD] PerplexityN', '[BOLD] Final AMI']
As suggested earlier in this subsection, BROWN achieves a higher average mutual information than BROWN_NW. This happens on both corpora. However, the situation is quite mixed when it comes to performance of language models. In 3 of the 4 setups, BROWN achieves better class prediction accuracy than BROWN_NW. The situation is different when considering the values for perplexity.