other,15-5-P03-1051,bq |
</term>
for automatically acquiring new
<term>
|
stems
|
</term>
from a 155 million
<term>
word
</term>
|
#4721
To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring newstems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
other,20-5-P03-1051,bq |
<term>
stems
</term>
from a 155 million
<term>
|
word
|
</term><term>
unsegmented corpus
</term>
,
|
#4726
To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 millionword unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
lr,21-5-P03-1051,bq |
from a 155 million
<term>
word
</term><term>
|
unsegmented corpus
|
</term>
, and re-estimate the
<term>
model
|
#4727
To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million wordunsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
other,27-5-P03-1051,bq |
corpus
</term>
, and re-estimate the
<term>
|
model parameters
|
</term>
with the expanded
<term>
vocabulary
|
#4733
To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate themodel parameters with the expanded vocabulary and training corpus. |
other,32-5-P03-1051,bq |
parameters
</term>
with the expanded
<term>
|
vocabulary
|
</term>
and
<term>
training corpus
</term>
.
|
#4738
To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expandedvocabulary and training corpus. |
lr,34-5-P03-1051,bq |
expanded
<term>
vocabulary
</term>
and
<term>
|
training corpus
|
</term>
. The resulting
<term>
Arabic word
|
#4740
To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary andtraining corpus. |
tech,2-6-P03-1051,bq |
training corpus
</term>
. The resulting
<term>
|
Arabic word segmentation system
|
</term>
achieves around 97 %
<term>
exact match
|
#4745
The resultingArabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens. |
measure(ment),10-6-P03-1051,bq |
system
</term>
achieves around 97 %
<term>
|
exact match accuracy
|
</term>
on a
<term>
test corpus
</term>
containing
|
#4753
The resulting Arabic word segmentation system achieves around 97%exact match accuracy on a test corpus containing 28,449 word tokens. |
lr,15-6-P03-1051,bq |
<term>
exact match accuracy
</term>
on a
<term>
|
test corpus
|
</term>
containing 28,449
<term>
word tokens
|
#4758
The resulting Arabic word segmentation system achieves around 97% exact match accuracy on atest corpus containing 28,449 word tokens. |
other,19-6-P03-1051,bq |
test corpus
</term>
containing 28,449
<term>
|
word tokens
|
</term>
. We believe this is a state-of-the-art
|
#4762
The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens. |
tech,9-7-P03-1051,bq |
state-of-the-art performance and the
<term>
|
algorithm
|
</term>
can be used for many
<term>
highly
|
#4774
We believe this is a state-of-the-art performance and thealgorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest. |
other,15-7-P03-1051,bq |
algorithm
</term>
can be used for many
<term>
|
highly inflected languages
|
</term>
provided that one can create a small
|
#4780
We believe this is a state-of-the-art performance and the algorithm can be used for manyhighly inflected languages provided that one can create a small manually segmented corpus of the language of interest. |
lr,25-7-P03-1051,bq |
provided that one can create a small
<term>
|
manually segmented corpus
|
</term>
of the
<term>
language
</term>
of interest
|
#4790
We believe this is a state-of-the-art performance and the algorithm can be used for many highly inflected languages provided that one can create a smallmanually segmented corpus of the language of interest. |
other,30-7-P03-1051,bq |
manually segmented corpus
</term>
of the
<term>
|
language
|
</term>
of interest . A central problem of
|
#4795
We believe this is a state-of-the-art performance and the algorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of thelanguage of interest. |