D08-1041 |
variants information in improving
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mention detection
|
system performance . Results
|
D08-1063 |
of each of these techniques on
|
mention detection
|
task performance . 5 Resources
|
D08-1063 |
propagation information in improving
|
mention detection
|
systems . Besides generic types
|
D08-1041 |
spelling variants in improving
|
mention detection
|
systems . We add a new feature
|
D08-1063 |
propagating information ( specifically
|
mention detection
|
information ) into such low resource
|
D08-1030 |
data is annotated primarily for a
|
mention detection
|
task which leads to the same
|
D08-1063 |
annotated corpus used to train the
|
mention detection
|
classifier does not have to be
|
D08-1063 |
resourcerich language and run
|
mention detection
|
. Then select the word sequences
|
D08-1063 |
x1 , x2 , guage . The goal of
|
mention detection
|
system is to find the most likely
|
D08-1063 |
effectiveness of our approach in improving
|
mention detection
|
system performance on languages
|
D08-1041 |
Experiments on Information Extraction
|
Mention detection
|
system experiments are conducted
|
D08-1063 |
evaluations . Also , the English
|
mention detection
|
system used for experiments has
|
D08-1063 |
Results show that the English
|
mention detection
|
system has a better performance
|
D08-1063 |
proposed in this article requires a
|
mention detection
|
system build in a resource-rich
|
D08-1063 |
• In English and in Spanish
|
mention detection
|
systems are similar to those
|
D08-1063 |
On the other hand , even though
|
mention detection
|
system is important for many
|
D08-1041 |
for testing . English and Arabic
|
mention detection
|
systems are using a large range
|
D08-1041 |
classical NLP tasks , we formulate the
|
mention detection
|
problem as a classification problem
|
D08-1063 |
mentioned in the introduction , the
|
mention detection
|
problem is formulated as a classification
|
D08-1063 |
that we are interested in the
|
mention detection
|
task only , we decided to use
|