Eckhard Bick - VISL

3.9 Performance: Measuring correctness
3.9.1 Training texts
Working on "known" bench mark texts of 10-20.000 words, by constantly testing rule
performance on manually introduced - markers, the Portuguese
morphological tagger (analyser and disambiguator together) can be geared to resolve
nearly all ambiguity while retaining a 99.9% correctness rate. For unknown texts,
results are obviously lower. Yet, performance on training texts is not irrelevant, since it
shows that the CG approach does not suffer from system immanent interference
problems to the same degree as, say, a probabilistic tagger based on a pure trigram
HMM, where (to my knowledge) even retraining and measuring on the same corpus
seldom yields more than 97% correctness, even for parts of speech.
Aiming at maximal precision, I have also worked on a larger, untagged text
(170.000 word from the Borba-Ramsey corpus) on both the morphological and syntactic
levels. Though it wasn't possible single-handedly to produce manually tagged
benchmark-corpora of that size, or to fully inspect the outcome of an automatic tagging
run, it still made sense automatically to extract and quantify surviving ambiguities after
tagging runs, since precision (defined as the percentage of surviving readings, that are
correct) can be approximated by minimising ambiguity, at least as long as intermittent
bench mark runs ensure that new rules discard few correct readings, and the ambiguity
percentage thus still remains high in comparison with the other factor in the precision
calculus, error frequency. With a PoS error rate of 1%, for instance, and 10% two-fold
ambiguity, precision would compute as 99/110 = 90%, and cutting ambiguity in half
(while retaining the same error rate) would entail a nearly equivalent improvement in
precision (99/105 94.3%). Surviving ambiguity, then, easily measured without manual
control on any text corpus, can be used as an approximate guide to how precision is
progressing during the grammar writing process. In contrast, recall (defined as the
percentage of correct readings, that survive disambiguation) has - in the absence of a
large tagged and proof-read Portuguese corpus for measuring - to be calculated
manually on smaller sample texts.
When forcing the parser into full disambiguation, where all words - with the
exception of the rare cases of true ambiguity - end up with one reading only, recall and
precision will obviously assume identical values, and one can regard the recall/precision
figure as a direct measure for the parser's performance, which is why I will henceforth
use the more general term correctness to mean recall/precision at 100%
disambiguation.
3.9.2 Test texts
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