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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.
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.
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,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.
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,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.
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.
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.
other,35-5-C04-1035,bq	be learnt automatically from these <term>	features	</term> . We suggest a new goal and evaluation	#5275 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 thesefeatures.
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,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.
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,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,13-5-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.
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.
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.
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,4-3-C04-1035,bq	Horn clauses </term> . We then use the <term>	predicates	</term> of such <term> clauses </term> to create	#5186 We then use thepredicates 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,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%.
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.


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