Eckhard Bick - VISL

is due to the fact that, in my CG-notation, subclause function is tagged as a number two tag onto
complementisers or main verbs, but will appear as its own node in constituent tree analysis (on
which the table is based). Though PALAVRAS tags co-ordinators for what they co-ordinate (e.g.
for direct object co-ordination), the CG-to-tree transformation program used in the
evaluation, did not yet handle co-ordination, so paratactic attachment was not quantified. A
distinction was made between clause- and group-level @PRED, but appositions (@APP) were
regarded as clause level constituents by the tree-generator.
The shaded columns (tag only) contain data directly reflecting CG tag output, while
the bold face columns (tag/attachment) show the decrease in performance if
attachment errors are counted, too, even where function tags are correct. The third
column type (attachment only), finally, reflects pure attachment performance,
judging the tree as such, without taking function tag errors into account.
For the system as a whole, PALAVRAS’ recall and precision converge on the
97% syntactic tag correctness mark known from other text samples (cp. chapter
3.9) 249 . The fact that not much (0.3%) is lost when pure attachment errors are
included is encouraging proof that CG-to-tree transformation is, in fact, feasible. A
recall and precision for dependency per se 250 of over 97.9% and 99.5%, respectively,
suggest that the attachment information contained in PALAVRAS’ output is actually
more robust than its function tag information.
There is a fair deal of variation in the specific performance data for individual
constituents. By comparison to the English FDG, PALAVRAS has a better recall for
@SUBJ and @ACC, and a slightly worse one for @SC.
Interestingly, subjects have a high precision and a (relatively) low recall, while
direct objects have a (relatively) low precision, but a high recall, suggesting that the
present rule set could be biased in favour of @ACC and against @SUBJ.
Apart from the @ACC - @SUBJ discrepancy, performance was best for verbal
function and subordinators (where the real disambiguation task resides in the
morphology), as well as prenominals (@>N) and arguments of prepositions (@P