measure(ment),10-6-P03-1051,bq |
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>
.
|
#4753
The resulting Arabic word segmentation system achieves around 97%exact match accuracy on a test corpus containing 28,449 word tokens. |
other,15-4-P03-1051,bq |
The
<term>
language model
</term>
is initially estimated from a small
<term>
manually segmented corpus
</term>
of about 110,000
<term>
words
</term>
.
|
#4704
The language model is initially estimated from a small manually segmented corpus of about 110,000words. |
other,19-6-P03-1051,bq |
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>
.
|
#4762
The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens. |
model,8-1-P03-1051,bq |
We approximate
<term>
Arabic 's rich morphology
</term>
by a
<term>
model
</term>
that a
<term>
word
</term>
consists of a sequence of
<term>
morphemes
</term>
in the
<term>
pattern
</term><term>
prefix * - stem-suffix *
</term>
( * denotes zero or more occurrences of a
<term>
morpheme
</term>
) .
|
#4608
We approximate Arabic's rich morphology by amodel that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
other,35-1-P03-1051,bq |
We approximate
<term>
Arabic 's rich morphology
</term>
by a
<term>
model
</term>
that a
<term>
word
</term>
consists of a sequence of
<term>
morphemes
</term>
in the
<term>
pattern
</term><term>
prefix * - stem-suffix *
</term>
( * denotes zero or more occurrences of a
<term>
morpheme
</term>
) .
|
#4635
We approximate Arabic'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 amorpheme). |
lr,15-6-P03-1051,bq |
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>
.
|
#4758
The resulting Arabic word segmentation system achieves around 97% exact match accuracy on atest corpus containing 28,449 word tokens. |
other,11-1-P03-1051,bq |
We approximate
<term>
Arabic 's rich morphology
</term>
by a
<term>
model
</term>
that a
<term>
word
</term>
consists of a sequence of
<term>
morphemes
</term>
in the
<term>
pattern
</term><term>
prefix * - stem-suffix *
</term>
( * denotes zero or more occurrences of a
<term>
morpheme
</term>
) .
|
#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). |
model,4-3-P03-1051,bq |
The
<term>
algorithm
</term>
uses a
<term>
trigram language model
</term>
to determine the most probable
<term>
morpheme sequence
</term>
for a given
<term>
input
</term>
.
|
#4674
The algorithm uses atrigram language model to determine the most probable morpheme sequence for a given input. |
tech,17-2-P03-1051,bq |
Our method is seeded by a small
<term>
manually segmented Arabic corpus
</term>
and uses it to bootstrap an
<term>
unsupervised algorithm
</term>
to build the
<term>
Arabic word segmenter
</term>
from a large
<term>
unsegmented Arabic corpus
</term>
.
|
#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. |
tech,9-5-P03-1051,bq |
To improve the
<term>
segmentation
</term><term>
accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a 155 million
<term>
word
</term><term>
unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training corpus
</term>
.
|
#4715
To improve the segmentation accuracy, we use anunsupervised 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. |
lr,34-5-P03-1051,bq |
To improve the
<term>
segmentation
</term><term>
accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a 155 million
<term>
word
</term><term>
unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training corpus
</term>
.
|
#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. |
other,2-1-P03-1051,bq |
We approximate
<term>
Arabic 's rich morphology
</term>
by a
<term>
model
</term>
that a
<term>
word
</term>
consists of a sequence of
<term>
morphemes
</term>
in the
<term>
pattern
</term><term>
prefix * - stem-suffix *
</term>
( * denotes zero or more occurrences of a
<term>
morpheme
</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). |
other,32-5-P03-1051,bq |
To improve the
<term>
segmentation
</term><term>
accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a 155 million
<term>
word
</term><term>
unsegmented corpus
</term>
, and re-estimate the
<term>
model 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. |
other,17-3-P03-1051,bq |
The
<term>
algorithm
</term>
uses a
<term>
trigram language model
</term>
to determine the most probable
<term>
morpheme sequence
</term>
for a given
<term>
input
</term>
.
|
#4687
The algorithm uses a trigram language model to determine the most probable morpheme sequence for a giveninput. |
lr,28-2-P03-1051,bq |
Our method is seeded by a small
<term>
manually segmented Arabic corpus
</term>
and uses it to bootstrap an
<term>
unsupervised algorithm
</term>
to build the
<term>
Arabic word segmenter
</term>
from a large
<term>
unsegmented Arabic corpus
</term>
.
|
#4666
Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a largeunsegmented Arabic corpus. |
other,15-7-P03-1051,bq |
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 small
<term>
manually segmented corpus
</term>
of the
<term>
language
</term>
of interest .
|
#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. |
other,20-5-P03-1051,bq |
To improve the
<term>
segmentation
</term><term>
accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a 155 million
<term>
word
</term><term>
unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training 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. |
other,15-5-P03-1051,bq |
To improve the
<term>
segmentation
</term><term>
accuracy
</term>
, we use an
<term>
unsupervised algorithm
</term>
for automatically acquiring new
<term>
stems
</term>
from a 155 million
<term>
word
</term><term>
unsegmented corpus
</term>
, and re-estimate the
<term>
model parameters
</term>
with the expanded
<term>
vocabulary
</term>
and
<term>
training corpus
</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,17-1-P03-1051,bq |
We approximate
<term>
Arabic 's rich morphology
</term>
by a
<term>
model
</term>
that a
<term>
word
</term>
consists of a sequence of
<term>
morphemes
</term>
in the
<term>
pattern
</term><term>
prefix * - stem-suffix *
</term>
( * denotes zero or more occurrences of a
<term>
morpheme
</term>
) .
|
#4617
We approximate Arabic's rich morphology by a model that a word consists of a sequence ofmorphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
other,21-1-P03-1051,bq |
We approximate
<term>
Arabic 's rich morphology
</term>
by a
<term>
model
</term>
that a
<term>
word
</term>
consists of a sequence of
<term>
morphemes
</term>
in the
<term>
pattern
</term><term>
prefix * - stem-suffix *
</term>
( * denotes zero or more occurrences of a
<term>
morpheme
</term>
) .
|
#4621
We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the patternprefix * - stem-suffix * (* denotes zero or more occurrences of a morpheme). |