The method combined the
<term>
log-likelihood under a baseline model
</term>
( that of Collins [ 1999 ] ) with evidence from an additional 500,000
<term>
features
</term>
over
<term>
parse trees
</term>
that were not included in the original
<term>
model
</term>
.
#8189The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model.
other,23-9-J05-1003,ak
The article also introduces a new
<term>
algorithm
</term>
for the
<term>
boosting approach
</term>
which takes advantage of the
<term>
sparsity
</term>
of the
<term>
feature space
</term>
in the
<term>
parsing data
</term>
.
#8249The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data .
tech,23-12-J05-1003,ak
Although the experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the approach should be applicable to many other
<term>
NLP problems
</term>
which are naturally framed as
<term>
ranking tasks
</term>
, for example ,
<term>
speech recognition
</term>
,
<term>
machine translation
</term>
, or
<term>
natural language generation
</term>
.
#8324Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.
