To improve the
<term>
segmentation accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a
<term>
155 million word unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training corpus
</term>
.
#4735To 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,ak
To improve the
<term>
segmentation accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a
<term>
155 million word unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training corpus
</term>
.
#4740To 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,ak
To improve the
<term>
segmentation accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a
<term>
155 million word unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training corpus
</term>
.
#4742To 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,ak
The resulting
<term>
Arabic word segmentation system
</term>
achieves around 97 %
<term>
exact match accuracy
</term>
on a
<term>
test corpus
</term>
containing 28,449
<term>
word tokens
</term>
.
#4747The 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,ak
The resulting
<term>
Arabic word segmentation system
</term>
achieves around 97 %
<term>
exact match accuracy
</term>
on a
<term>
test corpus
</term>
containing 28,449
<term>
word tokens
</term>
.
#4755The 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,ak
The resulting
<term>
Arabic word segmentation system
</term>
achieves around 97 %
<term>
exact match accuracy
</term>
on a
<term>
test corpus
</term>
containing 28,449
<term>
word tokens
</term>
.
#4760The resulting Arabic word segmentation system achieves around 97% exact match accuracy on atest corpus containing 28,449 word tokens.
other,19-6-P03-1051,ak
The resulting
<term>
Arabic word segmentation system
</term>
achieves around 97 %
<term>
exact match accuracy
</term>
on a
<term>
test corpus
</term>
containing 28,449
<term>
word tokens
</term>
.
#4764The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens.
tech,9-7-P03-1051,ak
We believe this is a state-of-the-art performance and the
<term>
algorithm
</term>
can be used for many
<term>
highly inflected languages
</term>
provided that one can create a
<term>
small manually segmented corpus
</term>
of the
<term>
language
</term>
of interest .
#4776We 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,ak
We believe this is a state-of-the-art performance and the
<term>
algorithm
</term>
can be used for many
<term>
highly inflected languages
</term>
provided that one can create a
<term>
small manually segmented corpus
</term>
of the
<term>
language
</term>
of interest .
#4782We 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,24-7-P03-1051,ak
We believe this is a state-of-the-art performance and the
<term>
algorithm
</term>
can be used for many
<term>
highly inflected languages
</term>
provided that one can create a
<term>
small manually segmented corpus
</term>
of the
<term>
language
</term>
of interest .
#4791We 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 asmall manually segmented corpus of the language of interest.
other,30-7-P03-1051,ak
We believe this is a state-of-the-art performance and the
<term>
algorithm
</term>
can be used for many
<term>
highly inflected languages
</term>
provided that one can create a
<term>
small manually segmented corpus
</term>
of the
<term>
language
</term>
of interest .
#4797We 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.