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,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,8-1-P03-1051,bq |
Arabic 's rich morphology
</term>
by a
<term>
|
model
|
</term>
that a
<term>
word
</term>
consists of
|
#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,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,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,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. |
other,17-1-P03-1051,bq |
word
</term>
consists of a sequence of
<term>
|
morphemes
|
</term>
in the
<term>
pattern
</term><term>
prefix
|
#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). |
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. |
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,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 theArabic word segmenter from a large unsegmented Arabic 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 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. |
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. |
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. |
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. |
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,21-1-P03-1051,bq |
morphemes
</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 patternprefix * - 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 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. |
other,17-3-P03-1051,bq |
morpheme sequence
</term>
for a given
<term>
|
input
|
</term>
. The
<term>
language model
</term>
is
|
#4687
The algorithm uses a trigram language model to determine the most probable morpheme sequence for a giveninput. |
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. |
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. |