Concordance

Query C04-1035 22 >
Random sample 22 (1,025.7 per million)

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.
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,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,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.
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.
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.
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,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.
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.
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.
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.
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.
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,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%.
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,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.
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.
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.
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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