tool,30-3-C04-1035,bq |
machine learning algorithms
</term>
:
<term>
|
SLIPPER
|
</term>
, a
<term>
rule-based learning algorithm
|
#5212
We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms:SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. |
tech,33-3-C04-1035,bq |
algorithms
</term>
:
<term>
SLIPPER
</term>
, a
<term>
|
rule-based learning algorithm
|
</term>
, and
<term>
TiMBL
</term>
, a
<term>
memory-based
|
#5215
We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, arule-based learning algorithm, and TiMBL, a memory-based system. |
tech,41-3-C04-1035,bq |
algorithm
</term>
, and
<term>
TiMBL
</term>
, a
<term>
|
memory-based system
|
</term>
. Both
<term>
learners
</term>
perform
|
#5223
We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, amemory-based system. |
tech,4-1-C04-1035,bq |
difference . This paper presents a
<term>
|
machine learning
|
</term>
approach to bare
<term>
sluice disambiguation
|
#5153
This paper presents amachine learning approach to bare sluice disambiguation in dialogue. |
tool,38-3-C04-1035,bq |
rule-based learning algorithm
</term>
, and
<term>
|
TiMBL
|
</term>
, a
<term>
memory-based system
</term>
|
#5220
We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, andTiMBL, a memory-based system. |
model,15-2-C04-1035,bq |
corpus-based sample and formulate them as
<term>
|
probabilistic Horn clauses
|
</term>
. We then use the
<term>
predicates
|
#5178
We extract a set of heuristic principles from a corpus-based sample and formulate them asprobabilistic Horn clauses. |
tech,9-1-C04-1035,bq |
machine learning
</term>
approach to bare
<term>
|
sluice disambiguation
|
</term>
in
<term>
dialogue
</term>
. We extract
|
#5158
This paper presents a machine learning approach to baresluice disambiguation in dialogue. |
tech,1-4-C04-1035,bq |
<term>
memory-based system
</term>
. Both
<term>
|
learners
|
</term>
perform well , yielding similar
<term>
|
#5227
Bothlearners perform well, yielding similar success rates of approx 90%. |
tech,26-3-C04-1035,bq |
dataset
</term>
, and run two different
<term>
|
machine learning algorithms
|
</term>
:
<term>
SLIPPER
</term>
, a
<term>
rule-based
|
#5208
We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two differentmachine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. |
other,12-1-C04-1035,bq |
<term>
sluice disambiguation
</term>
in
<term>
|
dialogue
|
</term>
. We extract a set of
<term>
heuristic
|
#5161
This paper presents a machine learning approach to bare sluice disambiguation indialogue. |
lr,20-3-C04-1035,bq |
features
</term>
to annotate an input
<term>
|
dataset
|
</term>
, and run two different
<term>
machine
|
#5202
We then use the predicates of such clauses to create a set of domain independent features to annotate an inputdataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. |
other,5-2-C04-1035,bq |
dialogue
</term>
. We extract a set of
<term>
|
heuristic principles
|
</term>
from a corpus-based sample and formulate
|
#5168
We extract a set ofheuristic principles from a corpus-based sample and formulate them as probabilistic Horn clauses. |
other,13-3-C04-1035,bq |
<term>
clauses
</term>
to create a set of
<term>
|
domain independent features
|
</term>
to annotate an input
<term>
dataset
|
#5195
We then use the predicates of such clauses to create a set ofdomain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. |
other,13-5-C04-1035,bq |
in terms of which we formulate our
<term>
|
heuristic principles
|
</term>
have significant
<term>
predictive
|
#5253
The results show that the features in terms of which we formulate ourheuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features. |
model,27-5-C04-1035,bq |
rules
</term>
that closely resemble our
<term>
|
Horn clauses
|
</term>
can be learnt automatically from
|
#5267
The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble ourHorn clauses can be learnt automatically from these features. |
other,17-5-C04-1035,bq |
principles
</term>
have significant
<term>
|
predictive power
|
</term>
, and that
<term>
rules
</term>
that
|
#5257
The results show that the features in terms of which we formulate our heuristic principles have significantpredictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features. |
measure(ment),7-4-C04-1035,bq |
</term>
perform well , yielding similar
<term>
|
success rates
|
</term>
of approx 90 % . The results show
|
#5233
Both learners perform well, yielding similarsuccess rates of approx 90%. |
other,7-3-C04-1035,bq |
the
<term>
predicates
</term>
of such
<term>
|
clauses
|
</term>
to create a set of
<term>
domain independent
|
#5189
We then use the predicates of suchclauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. |
other,22-5-C04-1035,bq |
<term>
predictive power
</term>
, and that
<term>
|
rules
|
</term>
that closely resemble our
<term>
Horn
|
#5262
The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and thatrules that closely resemble our Horn clauses can be learnt automatically from these features. |
other,5-5-C04-1035,bq |
approx 90 % . The results show that the
<term>
|
features
|
</term>
in terms of which we formulate our
|
#5245
The results show that thefeatures in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features. |