lr,10-5-N03-2025,bq |
Markov Model
</term>
is trained on a
<term>
|
corpus
|
</term>
automatically tagged by the first
|
#3368
Then, a Hidden Markov Model is trained on acorpus automatically tagged by the first learner. |
model,3-5-N03-2025,bq |
parsing-based NE rules
</term>
. Then , a
<term>
|
Hidden Markov Model
|
</term>
is trained on a
<term>
corpus
</term>
|
#3361
Then, aHidden Markov Model is trained on a corpus automatically tagged by the first learner. |
model,9-4-N03-2025,bq |
decision list
</term>
is used to learn the
<term>
|
parsing-based NE rules
|
</term>
. Then , a
<term>
Hidden Markov Model
|
#3354
First, decision list is used to learn theparsing-based NE rules. |
other,10-2-N03-2025,bq |
noun
</term>
or
<term>
pronoun
</term><term>
|
seeds
|
</term>
that correspond to the
<term>
concept
|
#3315
This approach only requires a few common noun or pronounseeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
other,10-6-N03-2025,bq |
supervised NE
</term>
performance for some
<term>
|
NE types
|
</term>
. In this paper , we describe a
<term>
|
#3386
The resulting NE system approaches supervised NE performance for someNE types. |
other,12-1-N03-2025,bq |
Entity ( NE ) tagging
</term>
using
<term>
|
concept-based seeds
|
</term>
and
<term>
successive learners
</term>
|
#3297
A novel bootstrapping approach to Named Entity (NE) tagging usingconcept-based seeds and successive learners is presented. |
other,15-2-N03-2025,bq |
seeds
</term>
that correspond to the
<term>
|
concept
|
</term>
for the targeted
<term>
NE
</term>
,
|
#3320
This approach only requires a few common noun or pronoun seeds that correspond to theconcept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
other,19-2-N03-2025,bq |
<term>
concept
</term>
for the targeted
<term>
|
NE
|
</term>
, e.g. he/she/man / woman for
<term>
|
#3324
This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targetedNE, e.g. he/she/man/woman for PERSON NE. |
other,26-2-N03-2025,bq |
</term>
, e.g. he/she/man / woman for
<term>
|
PERSON NE
|
</term>
. The
<term>
bootstrapping procedure
|
#3331
This approach only requires a few common noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman forPERSON NE. |
other,6-2-N03-2025,bq |
This approach only requires a few
<term>
|
common noun
|
</term>
or
<term>
pronoun
</term><term>
seeds
|
#3311
This approach only requires a fewcommon noun or pronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
other,9-2-N03-2025,bq |
requires a few
<term>
common noun
</term>
or
<term>
|
pronoun
|
</term><term>
seeds
</term>
that correspond
|
#3314
This approach only requires a few common noun orpronoun seeds that correspond to the concept for the targeted NE, e.g. he/she/man/woman for PERSON NE. |
tech,1-3-N03-2025,bq |
woman for
<term>
PERSON NE
</term>
. The
<term>
|
bootstrapping procedure
|
</term>
is implemented as training two
<term>
|
#3335
Thebootstrapping procedure is implemented as training two successive learners. |
tech,15-1-N03-2025,bq |
<term>
concept-based seeds
</term>
and
<term>
|
successive learners
|
</term>
is presented . This approach only
|
#3300
A novel bootstrapping approach to Named Entity (NE) tagging using concept-based seeds andsuccessive learners is presented. |
tech,16-5-N03-2025,bq |
</term>
automatically tagged by the first
<term>
|
learner
|
</term>
. The resulting
<term>
NE system
</term>
|
#3374
Then, a Hidden Markov Model is trained on a corpus automatically tagged by the firstlearner. |
tech,2-1-N03-2025,bq |
<term>
alignment quality
</term>
. A novel
<term>
|
bootstrapping approach
|
</term>
to
<term>
Named Entity ( NE ) tagging
|
#3287
A novelbootstrapping approach to Named Entity (NE) tagging using concept-based seeds and successive learners is presented. |
tech,2-4-N03-2025,bq |
successive learners
</term>
. First ,
<term>
|
decision list
|
</term>
is used to learn the
<term>
parsing-based
|
#3347
First,decision list is used to learn the parsing-based NE rules. |
tech,2-6-N03-2025,bq |
<term>
learner
</term>
. The resulting
<term>
|
NE system
|
</term>
approaches
<term>
supervised NE
</term>
|
#3378
The resultingNE system approaches supervised NE performance for some NE types. |
tech,5-1-N03-2025,bq |
<term>
bootstrapping approach
</term>
to
<term>
|
Named Entity ( NE ) tagging
|
</term>
using
<term>
concept-based seeds
</term>
|
#3290
A novel bootstrapping approach toNamed Entity ( NE ) tagging using concept-based seeds and successive learners is presented. |
tech,5-6-N03-2025,bq |
resulting
<term>
NE system
</term>
approaches
<term>
|
supervised NE
|
</term>
performance for some
<term>
NE types
|
#3381
The resulting NE system approachessupervised NE performance for some NE types. |
tech,8-3-N03-2025,bq |
</term>
is implemented as training two
<term>
|
successive learners
|
</term>
. First ,
<term>
decision list
</term>
|
#3342
The bootstrapping procedure is implemented as training twosuccessive learners. |