tech,0-2-P04-2010,bq |
This paper presents a novel
<term>
ensemble learning approach
</term>
to
<term>
resolving German pronouns
</term>
.
<term>
Boosting
</term>
, the method in question , combines the moderately accurate
<term>
hypotheses
</term>
of several
<term>
classifiers
</term>
to form a highly accurate one .
|
#7034
This paper presents a novel ensemble learning approach to resolving German pronouns.Boosting, the method in question, combines the moderately accurate hypotheses of several classifiers to form a highly accurate one. |
tech,26-4-P04-2010,bq |
Furthermore , we present a standalone system that resolves
<term>
pronouns
</term>
in
<term>
unannotated text
</term>
by using a fully automatic sequence of
<term>
preprocessing modules
</term>
that mimics the manual
<term>
annotation process
</term>
.
|
#7095
Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manualannotation process. |
tech,9-1-P04-2010,bq |
This paper presents a novel
<term>
ensemble learning approach
</term>
to
<term>
resolving German pronouns
</term>
.
|
#7030
This paper presents a novel ensemble learning approach toresolving German pronouns. |
tech,10-3-P04-2010,bq |
Experiments show that this approach is superior to a single
<term>
decision-tree classifier
</term>
.
|
#7066
Experiments show that this approach is superior to a singledecision-tree classifier. |
tech,24-5-P04-2010,bq |
Although the system performs well within a limited textual domain , further research is needed to make it effective for
<term>
open-domain question answering
</term>
and
<term>
text summarisation
</term>
.
|
#7122
Although the system performs well within a limited textual domain, further research is needed to make it effective for open-domain question answering andtext summarisation. |
tech,20-5-P04-2010,bq |
Although the system performs well within a limited textual domain , further research is needed to make it effective for
<term>
open-domain question answering
</term>
and
<term>
text summarisation
</term>
.
|
#7118
Although the system performs well within a limited textual domain, further research is needed to make it effective foropen-domain question answering and text summarisation. |
lr,11-4-P04-2010,bq |
Furthermore , we present a standalone system that resolves
<term>
pronouns
</term>
in
<term>
unannotated text
</term>
by using a fully automatic sequence of
<term>
preprocessing modules
</term>
that mimics the manual
<term>
annotation process
</term>
.
|
#7080
Furthermore, we present a standalone system that resolves pronouns inunannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. |
other,9-4-P04-2010,bq |
Furthermore , we present a standalone system that resolves
<term>
pronouns
</term>
in
<term>
unannotated text
</term>
by using a fully automatic sequence of
<term>
preprocessing modules
</term>
that mimics the manual
<term>
annotation process
</term>
.
|
#7078
Furthermore, we present a standalone system that resolvespronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process. |
other,11-2-P04-2010,bq |
<term>
Boosting
</term>
, the method in question , combines the moderately accurate
<term>
hypotheses
</term>
of several
<term>
classifiers
</term>
to form a highly accurate one .
|
#7045
Boosting, the method in question, combines the moderately accuratehypotheses of several classifiers to form a highly accurate one. |
tech,20-4-P04-2010,bq |
Furthermore , we present a standalone system that resolves
<term>
pronouns
</term>
in
<term>
unannotated text
</term>
by using a fully automatic sequence of
<term>
preprocessing modules
</term>
that mimics the manual
<term>
annotation process
</term>
.
|
#7089
Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence ofpreprocessing modules that mimics the manual annotation process. |
tech,14-2-P04-2010,bq |
<term>
Boosting
</term>
, the method in question , combines the moderately accurate
<term>
hypotheses
</term>
of several
<term>
classifiers
</term>
to form a highly accurate one .
|
#7048
Boosting, the method in question, combines the moderately accurate hypotheses of severalclassifiers to form a highly accurate one. |
tech,5-1-P04-2010,bq |
This paper presents a novel
<term>
ensemble learning approach
</term>
to
<term>
resolving German pronouns
</term>
.
|
#7026
This paper presents a novelensemble learning approach to resolving German pronouns. |