Longer words cannot be memorized in this representation (although common ends of words such as prefixes and suffixes might be). In order to test for this, Brent proposes that precision for word types (which he calls "lexicon precision") be measured as well as for word tokens. While the word-token metric emphasizes the correct segmentation of frequent words, the word-type metric does not share this bias. Brent defines this metric as follows: "After each block [of 500 utterances], each word type that the algorithm produced was labeled a true positive if that word type had occurred anywhere in the portion of the corpus processed so far; otherwise it is labeled a false positive. " Measured this way, MI yields a word type precision of only about 27%; transitional probability yields a precision of approximately 24% for the first 1000 utterances, compared to 42% for MBDP-1. He does not measure word type recall. This same limitation in finding longer, less frequent types may apply to comparisons against a global threshold as well. This is also in need of testing. It seems that both global and local comparisons, used on their own as sole or decisive heuristics, may have serious limitations. It is not clear a priori which limitation is most serious; hence both comparisons are tested here.