| lr,23-2-P06-1052,ak | to the <term> USRs </term> computed by <term> | large-scale grammars | </term> . We evaluate the <term> algorithm </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. | |
| lr,6-3-P06-1052,ak | evaluate the <term> algorithm </term> on a <term> | corpus | </term> , and show that it reduces the degree | #12120 We evaluate the algorithm on acorpus, and show that it reduces the degree of ambiguity significantly while taking negligible runtime. | |
| model,13-1-P06-1052,ak | elimination problem </term> : Given an <term> | underspecified semantic representation ( USR ) | </term> of a <term> scope ambiguity </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. | |
| model,20-2-P06-1052,ak | graphs </term> ; it can be applied to the <term> | USRs | </term> computed by <term> large-scale grammars | #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. | |
| model,26-1-P06-1052,ak | scope ambiguity </term> , compute an <term> | USR | </term> with fewer mutually equivalent <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. | |
| model,4-2-P06-1052,ak | The <term> algorithm </term> operates on <term> | underspecified chart representations | </term> which are derived from <term> dominance | #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 | representations </term> which are derived from <term> | dominance graphs | </term> ; it can be applied to the <term> USRs | #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. | |
| other,16-3-P06-1052,ak | show that it reduces the degree of <term> | ambiguity | </term> significantly while taking negligible | #12130 We evaluate the algorithm on a corpus, and show that it reduces the degree ofambiguity significantly while taking negligible runtime. | |
| other,21-1-P06-1052,ak | representation ( USR ) </term> of a <term> | scope ambiguity | </term> , compute an <term> USR </term> with | #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. | |
| other,21-3-P06-1052,ak | significantly while taking negligible <term> | runtime | </term> . In this paper , we describe the | #12135 We evaluate the algorithm on a corpus, and show that it reduces the degree of ambiguity significantly while taking negligibleruntime. | |
| other,31-1-P06-1052,ak | </term> with fewer mutually equivalent <term> | readings | </term> . The <term> algorithm </term> operates | #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. | |
| other,7-1-P06-1052,ak | efficient <term> algorithm </term> for the <term> | redundancy elimination problem | </term> : Given an <term> underspecified semantic | #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. | |
| tech,1-2-P06-1052,ak | equivalent <term> readings </term> . The <term> | algorithm | </term> operates on <term> underspecified chart | #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. | |
| tech,3-3-P06-1052,ak | large-scale grammars </term> . We evaluate the <term> | algorithm | </term> on a <term> corpus </term> , and show | #12117 We evaluate thealgorithm on a corpus, and show that it reduces the degree of ambiguity significantly while taking negligible runtime. | |
| tech,4-1-P06-1052,ak | LFG </term> . We present an efficient <term> | algorithm | </term> for the <term> redundancy elimination | #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. |
