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, a rule-based learning algorithm, and TiMBL, a memory-based system. |
tech,41-3-C04-1035,bq |
</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, a memory-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 a machine 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, and TiMBL , 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 as probabilistic 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 bare sluice disambiguation in dialogue. |
tech,1-4-C04-1035,bq |
<term>
memory-based system
</term>
. Both
<term>
|
learners
|
</term>
perform well , yielding similar
<term>
|
#5227
Both learners 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 different machine 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 in dialogue . |
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 input dataset , 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 of heuristic 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 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,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 our heuristic 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 our Horn 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 significant predictive 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 similar success 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 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. |
other,22-5-C04-1035,bq |
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 that rules that closely resemble our Horn clauses can be learnt automatically from these features. |
other,5-5-C04-1035,bq |
90 % . The results show that the
<term>
|
features
|
</term>
in terms of which we formulate our
|
#5245
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 our Horn clauses can be learnt automatically from these features. |