  This technical report is an appendix to Eisner (1996): it gives superior
experimental results that were reported only in the talk version of that paper.
Eisner (1996) trained three probability models on a small set of about 4,000
conjunction-free, dependency-grammar parses derived from the Wall Street
Journal section of the Penn Treebank, and then evaluated the models on a
held-out test set, using a novel O(n^3) parsing algorithm.
  The present paper describes some details of the experiments and repeats them
with a larger training set of 25,000 sentences. As reported at the talk, the
more extensive training yields greatly improved performance. Nearly half the
sentences are parsed with no misattachments; two-thirds are parsed with at most
one misattachment.
  Of the models described in the original written paper, the best score is
still obtained with the generative (top-down) "model C." However, slightly
better models are also explored, in particular, two variants on the
comprehension (bottom-up) "model B." The better of these has an attachment
accuracy of 90%, and (unlike model C) tags words more accurately than the
comparable trigram tagger. Differences are statistically significant.
  If tags are roughly known in advance, search error is all but eliminated and
the new model attains an attachment accuracy of 93%. We find that the parser of
Collins (1996), when combined with a highly-trained tagger, also achieves 93%
when trained and tested on the same sentences. Similarities and differences are
discussed.
