other,20-1-P03-1051,bq |
sequence of
<term>
morphemes
</term>
in the
<term>
|
pattern
|
</term><term>
prefix * - stem-suffix *
</term>
|
#4620
We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in thepattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
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. |
tech,1-3-P03-1051,bq |
unsegmented Arabic corpus
</term>
. The
<term>
|
algorithm
|
</term>
uses a
<term>
trigram language model
|
#4671
Thealgorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. |
model,1-4-P03-1051,bq |
for a given
<term>
input
</term>
. The
<term>
|
language model
|
</term>
is initially estimated from a small
|
#4690
Thelanguage model is initially estimated from a small manually segmented corpus of about 110,000 words. |
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. |
lr,9-4-P03-1051,bq |
is initially estimated from a small
<term>
|
manually segmented corpus
|
</term>
of about 110,000
<term>
words
</term>
|
#4698
The language model is initially estimated from a smallmanually segmented corpus of about 110,000 words. |
other,15-4-P03-1051,bq |
segmented corpus
</term>
of about 110,000
<term>
|
words
|
</term>
. To improve the
<term>
segmentation
|
#4704
The language model is initially estimated from a small manually segmented corpus of about 110,000words. |
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. |
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,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. |
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,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. |
measure(ment),4-5-P03-1051,bq |
improve the
<term>
segmentation
</term><term>
|
accuracy
|
</term>
, we use an
<term>
unsupervised algorithm
|
#4710
To improve the segmentationaccuracy, 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 and training corpus. |
other,11-1-P03-1051,bq |
</term>
by a
<term>
model
</term>
that a
<term>
|
word
|
</term>
consists of a sequence of
<term>
morphemes
|
#4611
We approximate Arabic's rich morphology by a model that aword consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
other,2-1-P03-1051,bq |
stemmer
</term>
above . We approximate
<term>
|
Arabic 's rich morphology
|
</term>
by a
<term>
model
</term>
that a
<term>
|
#4602
We approximateArabic 's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
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,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. |
tech,3-5-P03-1051,bq |
<term>
words
</term>
. To improve the
<term>
|
segmentation
|
</term><term>
accuracy
</term>
, we use an
<term>
|
#4709
To improve thesegmentation 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 and training corpus. |
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. |
tech,17-2-P03-1051,bq |
</term>
and uses it to bootstrap an
<term>
|
unsupervised algorithm
|
</term>
to build the
<term>
Arabic word segmenter
|
#4655
Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap anunsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. |