We present a new
<term>
part-of-speech tagger
</term>
that demonstrates the following ideas : ( i ) explicit use of both preceding and following
<term>
tag contexts
</term>
via a
<term>
dependency network representation
</term>
, ( ii ) broad use of
<term>
lexical features
</term>
, including jointly conditioning on multiple consecutive words , ( iii ) effective use of
<term>
priors
</term>
in
<term>
conditional loglinear models
</term>
, and ( iv ) fine-grained modeling of
<term>
unknown word features
</term>
.
#2914We present a newpart-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,ak
We present a new
<term>
part-of-speech tagger
</term>
that demonstrates the following ideas : ( i ) explicit use of both preceding and following
<term>
tag contexts
</term>
via a
<term>
dependency network representation
</term>
, ( ii ) broad use of
<term>
lexical features
</term>
, including jointly conditioning on multiple consecutive words , ( iii ) effective use of
<term>
priors
</term>
in
<term>
conditional loglinear models
</term>
, and ( iv ) fine-grained modeling of
<term>
unknown word features
</term>
.
#2932We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and followingtag 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,ak
We present a new
<term>
part-of-speech tagger
</term>
that demonstrates the following ideas : ( i ) explicit use of both preceding and following
<term>
tag contexts
</term>
via a
<term>
dependency network representation
</term>
, ( ii ) broad use of
<term>
lexical features
</term>
, including jointly conditioning on multiple consecutive words , ( iii ) effective use of
<term>
priors
</term>
in
<term>
conditional loglinear models
</term>
, and ( iv ) fine-grained modeling of
<term>
unknown word features
</term>
.
#2946We 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 oflexical 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,ak
We present a new
<term>
part-of-speech tagger
</term>
that demonstrates the following ideas : ( i ) explicit use of both preceding and following
<term>
tag contexts
</term>
via a
<term>
dependency network representation
</term>
, ( ii ) broad use of
<term>
lexical features
</term>
, including jointly conditioning on multiple consecutive words , ( iii ) effective use of
<term>
priors
</term>
in
<term>
conditional loglinear models
</term>
, and ( iv ) fine-grained modeling of
<term>
unknown word features
</term>
.
#2963We 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 ofpriors in conditional loglinear models, and (iv) fine-grained modeling of unknown word features.
model,55-1-N03-1033,ak
We present a new
<term>
part-of-speech tagger
</term>
that demonstrates the following ideas : ( i ) explicit use of both preceding and following
<term>
tag contexts
</term>
via a
<term>
dependency network representation
</term>
, ( ii ) broad use of
<term>
lexical features
</term>
, including jointly conditioning on multiple consecutive words , ( iii ) effective use of
<term>
priors
</term>
in
<term>
conditional loglinear models
</term>
, and ( iv ) fine-grained modeling of
<term>
unknown word features
</term>
.
#2965We 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 inconditional loglinear models, and (iv) fine-grained modeling of unknown word features.
other,66-1-N03-1033,ak
We present a new
<term>
part-of-speech tagger
</term>
that demonstrates the following ideas : ( i ) explicit use of both preceding and following
<term>
tag contexts
</term>
via a
<term>
dependency network representation
</term>
, ( ii ) broad use of
<term>
lexical features
</term>
, including jointly conditioning on multiple consecutive words , ( iii ) effective use of
<term>
priors
</term>
in
<term>
conditional loglinear models
</term>
, and ( iv ) fine-grained modeling of
<term>
unknown word features
</term>
.
#2976We 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 ofunknown word features.
tech,7-2-N03-1033,ak
Using these ideas together , the resulting
<term>
tagger
</term>
gives a 97.24 %
<term>
accuracy
</term>
on the
<term>
Penn Treebank WSJ
</term>
, an
<term>
error reduction
</term>
of 4.4 % on the best previous single
<term>
automatically learned tagging result
</term>
.
#2987Using these ideas together, the resultingtagger 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,ak
Using these ideas together , the resulting
<term>
tagger
</term>
gives a 97.24 %
<term>
accuracy
</term>
on the
<term>
Penn Treebank WSJ
</term>
, an
<term>
error reduction
</term>
of 4.4 % on the best previous single
<term>
automatically learned tagging result
</term>
.
#2992Using 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.
tool,15-2-N03-1033,ak
Using these ideas together , the resulting
<term>
tagger
</term>
gives a 97.24 %
<term>
accuracy
</term>
on the
<term>
Penn Treebank WSJ
</term>
, an
<term>
error reduction
</term>
of 4.4 % on the best previous single
<term>
automatically learned tagging result
</term>
.
#2995Using these ideas together, the resulting tagger gives a 97.24% accuracy on thePenn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result.
other,20-2-N03-1033,ak
Using these ideas together , the resulting
<term>
tagger
</term>
gives a 97.24 %
<term>
accuracy
</term>
on the
<term>
Penn Treebank WSJ
</term>
, an
<term>
error reduction
</term>
of 4.4 % on the best previous single
<term>
automatically learned tagging result
</term>
.
#3000Using these ideas together, the resulting tagger gives a 97.24% accuracy on the Penn Treebank WSJ, anerror reduction of 4.4% on the best previous single automatically learned tagging result.
other,30-2-N03-1033,ak
Using these ideas together , the resulting
<term>
tagger
</term>
gives a 97.24 %
<term>
accuracy
</term>
on the
<term>
Penn Treebank WSJ
</term>
, an
<term>
error reduction
</term>
of 4.4 % on the best previous single
<term>
automatically learned tagging result
</term>
.
#3010Using 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 singleautomatically learned tagging result.