#12059We 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
efficient
<term>
algorithm
</term>
for the
<term>
redundancy elimination problem
</term>
: Given an
<term>
underspecified semantic
#12062We 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
elimination problem
</term>
: Given an
<term>
underspecified semantic representation ( USR )
</term>
of a
<term>
scope ambiguity
</term>
,
#12068We 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
representation ( USR )
</term>
of a
<term>
scope ambiguity
</term>
, compute an
<term>
USR
</term>
with
#12076We 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
scope ambiguity
</term>
, compute an
<term>
USR
</term>
with fewer mutually equivalent
<term>
#12081We 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
</term>
with fewer mutually equivalent
<term>
readings
</term>
. The
<term>
algorithm
</term>
operates
#12086We 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
equivalent
<term>
readings
</term>
. The
<term>
algorithm
</term>
operates on
<term>
underspecified chart
#12089Thealgorithm 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
#12092The 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
#12099The 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
graphs
</term>
; it can be applied to the
<term>
USRs
</term>
computed by
<term>
large-scale grammars
#12108The 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
to the
<term>
USRs
</term>
computed by
<term>
large-scale grammars
</term>
. We evaluate the
<term>
algorithm
</term>
#12111The 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
large-scale grammars
</term>
. We evaluate the
<term>
algorithm
</term>
on a
<term>
corpus
</term>
, and show
#12117We 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
evaluate the
<term>
algorithm
</term>
on a
<term>
corpus
</term>
, and show that it reduces the degree
#12120We 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
show that it reduces the degree of
<term>
ambiguity
</term>
significantly while taking negligible
#12130We 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
significantly while taking negligible
<term>
runtime
</term>
. In this paper , we describe the
#12135We evaluate the algorithm on a corpus, and show that it reduces the degree of ambiguity significantly while taking negligibleruntime.