Eckhard Bick - VISL

N 107 - - - - - - - 107 99.7
ADJ 2 - - - - 4 - 6 99.9
VFIN 14 - 2 - 16 99.9
INF 2 - - - - 2 100.0
GER - - - - - 100.0
PCP 13 - - 13 99.7
ADV 9 - 15 99.8
PROP 10 10 99.9
all 163 99.8
before: 69603 17950 30619 4970 903 5335 13938 11704 121170
after: 39394 9549 16023 4648 894 3818 8552 11522 94394
decrease 43.4 46.8 47.7 6.5 1 28.4 38.6 1.6 22.1
(in %):
table 2
ambiguity
index
42.2 91.5 96.0 93.8 3.9 94.3 50.4 32.2 45.1
Cross word class precision is virtually 100% for all open word classes, with the only
exception of the - not so open - adverb class (99.8%). But even when including word
class internal ambiguity, precision is still as high as 99.8%.
Table (6) makes it clear, how huge the differences in "disambiguation gain" are
for the different word classes, suggesting how and where it would be most economical
for the grammarian to channel his rule writing effort. Very little is gained for proper
nouns, infinitives and gerunds, while finite verbs, nouns and adjectives have a nearly
50% disambiguation gain. From this it is clear that it "pays more" to write CG rules
aimed at the latter classes than for the first.
Even more striking is a look at the relations between ambiguity index and
disambiguation gain: infinitives, for example, start as highly ambiguous word forms,
but most cases are finally tagged as unambiguous infinitives anyway! For nouns, though
not as ambiguous to begin with, the disambiguation tendency is even more lopsided,
with an ambiguity index 20-times as high as the final disambiguation gain, meaning that
there is a very strong bias in favour of the PROP reading in ambiguous cases. The most
"profitable" situation is that encountered in nouns, where CG rules do most work: the
gain percentage is about the same as the ambiguity index, meaning that nouns have no
strong bias in their ambiguity distribution.
- 123 -