tech,4-1-C04-1080,bq |
We present a new
<term>
HMM tagger
</term>
that exploits
<term>
context
</term>
on both sides of a
<term>
word
</term>
to be tagged , and evaluate it in both the
<term>
unsupervised and supervised case
</term>
.
|
#5500
We present a newHMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. |
other,8-1-C04-1080,bq |
We present a new
<term>
HMM tagger
</term>
that exploits
<term>
context
</term>
on both sides of a
<term>
word
</term>
to be tagged , and evaluate it in both the
<term>
unsupervised and supervised case
</term>
.
|
#5504
We present a new HMM tagger that exploitscontext on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. |
other,14-1-C04-1080,bq |
We present a new
<term>
HMM tagger
</term>
that exploits
<term>
context
</term>
on both sides of a
<term>
word
</term>
to be tagged , and evaluate it in both the
<term>
unsupervised and supervised case
</term>
.
|
#5510
We present a new HMM tagger that exploits context on both sides of aword to be tagged, and evaluate it in both the unsupervised and supervised case. |
other,25-1-C04-1080,bq |
We present a new
<term>
HMM tagger
</term>
that exploits
<term>
context
</term>
on both sides of a
<term>
word
</term>
to be tagged , and evaluate it in both the
<term>
unsupervised and supervised case
</term>
.
|
#5521
We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both theunsupervised and supervised case. |
tech,11-2-C04-1080,bq |
Along the way , we present the first comprehensive comparison of
<term>
unsupervised methods for part-of-speech tagging
</term>
, noting that published results to date have not been comparable across
<term>
corpora
</term>
or
<term>
lexicons
</term>
.
|
#5537
Along the way, we present the first comprehensive comparison ofunsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons. |
lr,28-2-C04-1080,bq |
Along the way , we present the first comprehensive comparison of
<term>
unsupervised methods for part-of-speech tagging
</term>
, noting that published results to date have not been comparable across
<term>
corpora
</term>
or
<term>
lexicons
</term>
.
|
#5554
Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable acrosscorpora or lexicons. |
lr,30-2-C04-1080,bq |
Along the way , we present the first comprehensive comparison of
<term>
unsupervised methods for part-of-speech tagging
</term>
, noting that published results to date have not been comparable across
<term>
corpora
</term>
or
<term>
lexicons
</term>
.
|
#5556
Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora orlexicons. |
lr,6-3-C04-1080,bq |
Observing that the quality of the
<term>
lexicon
</term>
greatly impacts the
<term>
accuracy
</term>
that can be achieved by the
<term>
algorithms
</term>
, we present a method of
<term>
HMM training
</term>
that improves
<term>
accuracy
</term>
when training of
<term>
lexical probabilities
</term>
is unstable .
|
#5564
Observing that the quality of thelexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. |
measure(ment),10-3-C04-1080,bq |
Observing that the quality of the
<term>
lexicon
</term>
greatly impacts the
<term>
accuracy
</term>
that can be achieved by the
<term>
algorithms
</term>
, we present a method of
<term>
HMM training
</term>
that improves
<term>
accuracy
</term>
when training of
<term>
lexical probabilities
</term>
is unstable .
|
#5568
Observing that the quality of the lexicon greatly impacts theaccuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. |
tech,17-3-C04-1080,bq |
Observing that the quality of the
<term>
lexicon
</term>
greatly impacts the
<term>
accuracy
</term>
that can be achieved by the
<term>
algorithms
</term>
, we present a method of
<term>
HMM training
</term>
that improves
<term>
accuracy
</term>
when training of
<term>
lexical probabilities
</term>
is unstable .
|
#5575
Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by thealgorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. |
tech,24-3-C04-1080,bq |
Observing that the quality of the
<term>
lexicon
</term>
greatly impacts the
<term>
accuracy
</term>
that can be achieved by the
<term>
algorithms
</term>
, we present a method of
<term>
HMM training
</term>
that improves
<term>
accuracy
</term>
when training of
<term>
lexical probabilities
</term>
is unstable .
|
#5582
Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method ofHMM training that improves accuracy when training of lexical probabilities is unstable. |
measure(ment),28-3-C04-1080,bq |
Observing that the quality of the
<term>
lexicon
</term>
greatly impacts the
<term>
accuracy
</term>
that can be achieved by the
<term>
algorithms
</term>
, we present a method of
<term>
HMM training
</term>
that improves
<term>
accuracy
</term>
when training of
<term>
lexical probabilities
</term>
is unstable .
|
#5586
Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improvesaccuracy when training of lexical probabilities is unstable. |
other,32-3-C04-1080,bq |
Observing that the quality of the
<term>
lexicon
</term>
greatly impacts the
<term>
accuracy
</term>
that can be achieved by the
<term>
algorithms
</term>
, we present a method of
<term>
HMM training
</term>
that improves
<term>
accuracy
</term>
when training of
<term>
lexical probabilities
</term>
is unstable .
|
#5590
Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training oflexical probabilities is unstable. |
tech,7-4-C04-1080,bq |
Finally , we show how this new
<term>
tagger
</term>
achieves state-of-the-art results in a
<term>
supervised , non-training intensive framework
</term>
.
|
#5602
Finally, we show how this newtagger achieves state-of-the-art results in a supervised, non-training intensive framework. |
tech,13-4-C04-1080,bq |
Finally , we show how this new
<term>
tagger
</term>
achieves state-of-the-art results in a
<term>
supervised , non-training intensive framework
</term>
.
|
#5608
Finally, we show how this new tagger achieves state-of-the-art results in asupervised , non-training intensive framework. |