Concordance

Query P06-1052 15 (480.2 per million)

tech,4-1-P06-1052,ak	We present an efficient <term> algorithm </term> for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually equivalent <term> readings </term> .	#12059 We present an efficientalgorithm for the redundancy elimination problem: Given an underspecified semantic representation (USR) of a scope ambiguity, compute an USR with fewer mutually equivalent readings.
other,7-1-P06-1052,ak	We present an efficient <term> algorithm </term> for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually equivalent <term> readings </term> .	#12062 We present an efficient algorithm for theredundancy elimination problem: Given an underspecified semantic representation (USR) of a scope ambiguity, compute an USR with fewer mutually equivalent readings.
model,13-1-P06-1052,ak	We present an efficient <term> algorithm </term> for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually equivalent <term> readings </term> .	#12068 We present an efficient algorithm for the redundancy elimination problem: Given anunderspecified semantic representation ( USR ) of a scope ambiguity, compute an USR with fewer mutually equivalent readings.
other,21-1-P06-1052,ak	We present an efficient <term> algorithm </term> for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually equivalent <term> readings </term> .	#12076 We present an efficient algorithm for the redundancy elimination problem: Given an underspecified semantic representation (USR) of ascope ambiguity, compute an USR with fewer mutually equivalent readings.
model,26-1-P06-1052,ak	We present an efficient <term> algorithm </term> for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually equivalent <term> readings </term> .	#12081 We present an efficient algorithm for the redundancy elimination problem: Given an underspecified semantic representation (USR) of a scope ambiguity, compute anUSR with fewer mutually equivalent readings.
other,31-1-P06-1052,ak	We present an efficient <term> algorithm </term> for the <term> redundancy elimination problem </term> : Given an <term> underspecified semantic representation ( USR ) </term> of a <term> scope ambiguity </term> , compute an <term> USR </term> with fewer mutually equivalent <term> readings </term> .	#12086 We present an efficient algorithm for the redundancy elimination problem: Given an underspecified semantic representation (USR) of a scope ambiguity, compute an USR with fewer mutually equivalentreadings.
tech,1-2-P06-1052,ak	The <term> algorithm </term> operates on <term> underspecified chart representations </term> which are derived from <term> dominance graphs </term> ; it can be applied to the <term> USRs </term> computed by <term> large-scale grammars </term> .	#12089 Thealgorithm operates on underspecified chart representations which are derived from dominance graphs; it can be applied to the USRs computed by large-scale grammars.
model,4-2-P06-1052,ak	The <term> algorithm </term> operates on <term> underspecified chart representations </term> which are derived from <term> dominance graphs </term> ; it can be applied to the <term> USRs </term> computed by <term> large-scale grammars </term> .	#12092 The algorithm operates onunderspecified chart representations which are derived from dominance graphs; it can be applied to the USRs computed by large-scale grammars.
other,11-2-P06-1052,ak	The <term> algorithm </term> operates on <term> underspecified chart representations </term> which are derived from <term> dominance graphs </term> ; it can be applied to the <term> USRs </term> computed by <term> large-scale grammars </term> .	#12099 The algorithm operates on underspecified chart representations which are derived fromdominance graphs; it can be applied to the USRs computed by large-scale grammars.
model,20-2-P06-1052,ak	The <term> algorithm </term> operates on <term> underspecified chart representations </term> which are derived from <term> dominance graphs </term> ; it can be applied to the <term> USRs </term> computed by <term> large-scale grammars </term> .	#12108 The algorithm operates on underspecified chart representations which are derived from dominance graphs; it can be applied to theUSRs computed by large-scale grammars.
lr,23-2-P06-1052,ak	The <term> algorithm </term> operates on <term> underspecified chart representations </term> which are derived from <term> dominance graphs </term> ; it can be applied to the <term> USRs </term> computed by <term> large-scale grammars </term> .	#12111 The algorithm operates on underspecified chart representations which are derived from dominance graphs; it can be applied to the USRs computed bylarge-scale grammars.
tech,3-3-P06-1052,ak	We evaluate the <term> algorithm </term> on a <term> corpus </term> , and show that it reduces the degree of <term> ambiguity </term> significantly while taking negligible <term> runtime </term> .	#12117 We evaluate thealgorithm on a corpus, and show that it reduces the degree of ambiguity significantly while taking negligible runtime.
lr,6-3-P06-1052,ak	We evaluate the <term> algorithm </term> on a <term> corpus </term> , and show that it reduces the degree of <term> ambiguity </term> significantly while taking negligible <term> runtime </term> .	#12120 We evaluate the algorithm on acorpus, and show that it reduces the degree of ambiguity significantly while taking negligible runtime.
other,16-3-P06-1052,ak	We evaluate the <term> algorithm </term> on a <term> corpus </term> , and show that it reduces the degree of <term> ambiguity </term> significantly while taking negligible <term> runtime </term> .	#12130 We evaluate the algorithm on a corpus, and show that it reduces the degree ofambiguity significantly while taking negligible runtime.
other,21-3-P06-1052,ak	We evaluate the <term> algorithm </term> on a <term> corpus </term> , and show that it reduces the degree of <term> ambiguity </term> significantly while taking negligible <term> runtime </term> .	#12135 We evaluate the algorithm on a corpus, and show that it reduces the degree of ambiguity significantly while taking negligibleruntime.


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