tech,12-2-C04-1112,bq |
ambiguous wordform
</term>
, we introduce a
<term>
|
lemma-based approach
|
</term>
. The advantage of this novel method
|
#6021
Instead of building individual classifiers per ambiguous wordform, we introduce alemma-based approach. |
tech,7-1-C04-1112,bq |
</term>
. In this paper , we present a
<term>
|
corpus-based supervised word sense disambiguation ( WSD ) system
|
</term>
for
<term>
Dutch
</term>
which combines
|
#5986
In this paper, we present acorpus-based supervised word sense disambiguation ( WSD ) system for Dutch which combines statistical classification (maximum entropy) with linguistic information. |
other,11-3-C04-1112,bq |
novel method is that it clusters all
<term>
|
inflected forms
|
</term>
of an
<term>
ambiguous word
</term>
in
|
#6035
The advantage of this novel method is that it clusters allinflected forms of an ambiguous word in one classifier, therefore augmenting the training material available to the algorithm. |
other,15-3-C04-1112,bq |
all
<term>
inflected forms
</term>
of an
<term>
|
ambiguous word
|
</term>
in one
<term>
classifier
</term>
, therefore
|
#6039
The advantage of this novel method is that it clusters all inflected forms of anambiguous word in one classifier, therefore augmenting the training material available to the algorithm. |
tech,20-1-C04-1112,bq |
for
<term>
Dutch
</term>
which combines
<term>
|
statistical classification ( maximum entropy )
|
</term>
with
<term>
linguistic information
</term>
|
#5999
In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combinesstatistical classification ( maximum entropy ) with linguistic information. |
other,17-1-C04-1112,bq |
disambiguation ( WSD ) system
</term>
for
<term>
|
Dutch
|
</term>
which combines
<term>
statistical classification
|
#5996
In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system forDutch which combines statistical classification (maximum entropy) with linguistic information. |
measure(ment),17-4-C04-1112,bq |
achieve a significant increase in
<term>
|
accuracy
|
</term>
over the
<term>
wordform model
</term>
|
#6072
Testing the lemma-based model on the Dutch SENSEVAL-2 test data, we achieve a significant increase inaccuracy over the wordform model. |
tech,4-2-C04-1112,bq |
</term>
. Instead of building individual
<term>
|
classifiers
|
</term>
per
<term>
ambiguous wordform
</term>
|
#6013
Instead of building individualclassifiers per ambiguous wordform, we introduce a lemma-based approach. |
tech,19-3-C04-1112,bq |
an
<term>
ambiguous word
</term>
in one
<term>
|
classifier
|
</term>
, therefore augmenting the
<term>
training
|
#6043
The advantage of this novel method is that it clusters all inflected forms of an ambiguous word in oneclassifier, therefore augmenting the training material available to the algorithm. |
other,6-2-C04-1112,bq |
individual
<term>
classifiers
</term>
per
<term>
|
ambiguous wordform
|
</term>
, we introduce a
<term>
lemma-based
|
#6015
Instead of building individual classifiers perambiguous wordform, we introduce a lemma-based approach. |
tech,3-5-C04-1112,bq |
<term>
wordform model
</term>
. Also , the
<term>
|
WSD system based on lemmas
|
</term>
is smaller and more robust . We present
|
#6081
Also, theWSD system based on lemmas is smaller and more robust. |
lr-prod,6-4-C04-1112,bq |
<term>
lemma-based model
</term>
on the
<term>
|
Dutch SENSEVAL-2 test data
|
</term>
, we achieve a significant increase
|
#6061
Testing the lemma-based model on theDutch SENSEVAL-2 test data, we achieve a significant increase in accuracy over the wordform model. |
tech,20-4-C04-1112,bq |
increase in
<term>
accuracy
</term>
over the
<term>
|
wordform model
|
</term>
. Also , the
<term>
WSD system based
|
#6075
Testing the lemma-based model on the Dutch SENSEVAL-2 test data, we achieve a significant increase in accuracy over thewordform model. |
tech,2-4-C04-1112,bq |
available to the algorithm . Testing the
<term>
|
lemma-based model
|
</term>
on the
<term>
Dutch SENSEVAL-2 test
|
#6057
Testing thelemma-based model on the Dutch SENSEVAL-2 test data, we achieve a significant increase in accuracy over the wordform model. |
other,27-1-C04-1112,bq |
classification ( maximum entropy )
</term>
with
<term>
|
linguistic information
|
</term>
. Instead of building individual
<term>
|
#6006
In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) withlinguistic information. |