tech,4-1-N03-1033,bq |
parser/generator
</term>
. We present a new
<term>
|
part-of-speech
tagger
|
</term>
that demonstrates the following ideas
|
#2913
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
other,22-1-N03-1033,bq |
use of both preceding and following
<term>
|
tag
contexts
|
</term>
via a
<term>
dependency network representation
|
#2931
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
other,26-1-N03-1033,bq |
following
<term>
tag contexts
</term>
via a
<term>
|
dependency
network representation
|
</term>
, ( ii ) broad use of
<term>
lexical
|
#2935
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
other,36-1-N03-1033,bq |
representation
</term>
, ( ii ) broad use of
<term>
|
lexical
features
|
</term>
, including
<term>
jointly conditioning
|
#2945
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
tech,40-1-N03-1033,bq |
lexical features
</term>
, including
<term>
|
jointly
conditioning on multiple consecutive words
|
</term>
, ( iii ) effective use of
<term>
priors
|
#2949
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
other,53-1-N03-1033,bq |
</term>
, ( iii ) effective use of
<term>
|
priors
|
</term>
in
<term>
conditional loglinear models
|
#2962
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
model,55-1-N03-1033,bq |
effective use of
<term>
priors
</term>
in
<term>
|
conditional
loglinear models
|
</term>
, and ( iv ) fine-grained modeling
|
#2964
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
other,66-1-N03-1033,bq |
and ( iv ) fine-grained modeling of
<term>
|
unknown
word features
|
</term>
. Using these ideas together , the
|
#2975
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representation, (ii) broad use of lexical features, including jointly conditioning on multiple consecutive words, (iii) effective use of priors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features. |
tech,7-2-N03-1033,bq |
these ideas together , the resulting
<term>
|
tagger
|
</term>
gives a 97.24 %
<term>
accuracy
</term>
|
#2986
Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. |
measure(ment),12-2-N03-1033,bq |
<term>
tagger
</term>
gives a 97.24 %
<term>
|
accuracy
|
</term>
on the
<term>
Penn Treebank WSJ
</term>
|
#2991
Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. |
lr-prod,15-2-N03-1033,bq |
97.24 %
<term>
accuracy
</term>
on the
<term>
|
Penn
Treebank WSJ
|
</term>
, an
<term>
error reduction
</term>
of
|
#2994
Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. |
measure(ment),20-2-N03-1033,bq |
<term>
Penn Treebank WSJ
</term>
, an
<term>
|
error
reduction
|
</term>
of 4.4 % on the best previous single
|
#2999
Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. |
tech,32-2-N03-1033,bq |
previous single automatically learned
<term>
|
tagging
|
</term>
result . Sources of
<term>
training
|
#3011
Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. |