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 and training corpus. |
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 small manually segmented corpus of the language of interest. |
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 110,000 words . |
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. |
other,17-1-P03-1051,bq |
word
</term>
consists of a sequence of
<term>
|
morphemes
|
</term>
in the
<term>
pattern
</term><term>
|
#4617
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 a morpheme). |
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 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 and training corpus. |
tech,9-5-P03-1051,bq |
</term><term>
accuracy
</term>
, we use an
<term>
|
unsupervised
algorithm
|
</term>
for automatically acquiring new
<term>
|
#4715
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 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 many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest. |
other,21-1-P03-1051,bq |
</term>
in the
<term>
pattern
</term>
<term>
|
prefix
* - stem-suffix *
|
</term>
( * denotes zero or more occurrences
|
#4621
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 a morpheme). |
other,35-1-P03-1051,bq |
denotes zero or more occurrences of a
<term>
|
morpheme
|
</term>
) . Our method is seeded by a small
|
#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 a morpheme ). |
other,30-7-P03-1051,bq |
manually segmented corpus
</term>
of the
<term>
|
language
|
</term>
of interest . A central problem
|
#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 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 an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. |
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 a test corpus containing 28,449 word tokens. |
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 the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
model,8-1-P03-1051,bq |
Arabic 's rich morphology
</term>
by a
<term>
|
model
|
</term>
that a
<term>
word
</term>
consists
|
#4608
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 a morpheme). |
tech,1-3-P03-1051,bq |
unsegmented Arabic corpus
</term>
. The
<term>
|
algorithm
|
</term>
uses a
<term>
trigram language model
|
#4671
The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. |
tech,22-2-P03-1051,bq |
unsupervised algorithm
</term>
to build the
<term>
|
Arabic
word segmenter
|
</term>
from a large
<term>
unsegmented Arabic
|
#4660
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 large unsegmented Arabic 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 word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
lr,28-2-P03-1051,bq |
word segmenter
</term>
from a large
<term>
|
unsegmented
Arabic corpus
|
</term>
. The
<term>
algorithm
</term>
uses a
|
#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 large unsegmented Arabic corpus. |
tech,3-5-P03-1051,bq |
<term>
words
</term>
. To improve the
<term>
|
segmentation
|
</term>
<term>
accuracy
</term>
, we use an
|
#4709
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 and training corpus. |