| other,7-1-P06-1052,bq | present an efficient algorithm for the <term> | redundancy elimination problem | </term> : Given an <term> underspecified semantic | #11125 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. |
| other,13-1-P06-1052,bq | elimination problem </term> : Given an <term> | underspecified semantic representation ( USR ) | </term> of a <term> scope ambiguity </term> , | #11131 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,bq | representation ( USR ) </term> of a <term> | scope ambiguity | </term> , compute an <term> USR </term> with | #11139 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,26-1-P06-1052,bq | scope ambiguity </term> , compute an <term> | USR | </term> with fewer mutually <term> equivalent | #11144 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,30-1-P06-1052,bq | <term> USR </term> with fewer mutually <term> | equivalent readings | </term> . The algorithm operates on <term> | #11148 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 mutuallyequivalent readings. |
| other,4-2-P06-1052,bq | </term> . The algorithm operates on <term> | underspecified chart representations | </term> which are derived from <term> dominance | #11155 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,bq | representations </term> which are derived from <term> | dominance graphs | </term> ; it can be applied to the <term> USRs | #11162 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,20-2-P06-1052,bq | graphs </term> ; it can be applied to the <term> | USRs | </term> computed by <term> large-scale grammars | #11171 The algorithm operates on underspecified chart representations which are derived from dominance graphs; it can be applied to theUSRs computed by large-scale grammars. |
| other,23-2-P06-1052,bq | to the <term> USRs </term> computed by <term> | large-scale grammars | </term> . We evaluate the algorithm on a <term> | #11174 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,bq | </term> . We evaluate the algorithm on a <term> | corpus | </term> , and show that it reduces the degree | #11183 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,bq | show that it reduces the degree of <term> | ambiguity | </term> significantly while taking negligible | #11193 We evaluate the algorithm on a corpus, and show that it reduces the degree ofambiguity significantly while taking negligible runtime. |
