Although adequate models of
<term>
human language
</term>
for
<term>
syntactic analysis
</term>
and
<term>
semantic interpretation
</term>
are of at least
<term>
context-free complexity
</term>
, for applications such as
<term>
speech processing
</term>
in which speed is important
<term>
finite-state models
</term>
are often preferred .
#29773Although adequate models ofhuman language for syntactic analysis and semantic interpretation are of at least context-free complexity, for applications such as speech processing in which speed is important finite-state models are often preferred.
tech,7-1-P97-1058,ak
Although adequate models of
<term>
human language
</term>
for
<term>
syntactic analysis
</term>
and
<term>
semantic interpretation
</term>
are of at least
<term>
context-free complexity
</term>
, for applications such as
<term>
speech processing
</term>
in which speed is important
<term>
finite-state models
</term>
are often preferred .
#29776Although adequate models of human language forsyntactic analysis and semantic interpretation are of at least context-free complexity, for applications such as speech processing in which speed is important finite-state models are often preferred.
tech,10-1-P97-1058,ak
Although adequate models of
<term>
human language
</term>
for
<term>
syntactic analysis
</term>
and
<term>
semantic interpretation
</term>
are of at least
<term>
context-free complexity
</term>
, for applications such as
<term>
speech processing
</term>
in which speed is important
<term>
finite-state models
</term>
are often preferred .
#29779Although adequate models of human language for syntactic analysis andsemantic interpretation are of at least context-free complexity, for applications such as speech processing in which speed is important finite-state models are often preferred.
other,16-1-P97-1058,ak
Although adequate models of
<term>
human language
</term>
for
<term>
syntactic analysis
</term>
and
<term>
semantic interpretation
</term>
are of at least
<term>
context-free complexity
</term>
, for applications such as
<term>
speech processing
</term>
in which speed is important
<term>
finite-state models
</term>
are often preferred .
#29785Although adequate models of human language for syntactic analysis and semantic interpretation are of at leastcontext-free complexity, for applications such as speech processing in which speed is important finite-state models are often preferred.
tech,23-1-P97-1058,ak
Although adequate models of
<term>
human language
</term>
for
<term>
syntactic analysis
</term>
and
<term>
semantic interpretation
</term>
are of at least
<term>
context-free complexity
</term>
, for applications such as
<term>
speech processing
</term>
in which speed is important
<term>
finite-state models
</term>
are often preferred .
#29792Although adequate models of human language for syntactic analysis and semantic interpretation are of at least context-free complexity, for applications such asspeech processing in which speed is important finite-state models are often preferred.
tech,30-1-P97-1058,ak
Although adequate models of
<term>
human language
</term>
for
<term>
syntactic analysis
</term>
and
<term>
semantic interpretation
</term>
are of at least
<term>
context-free complexity
</term>
, for applications such as
<term>
speech processing
</term>
in which speed is important
<term>
finite-state models
</term>
are often preferred .
#29799Although adequate models of human language for syntactic analysis and semantic interpretation are of at least context-free complexity, for applications such as speech processing in which speed is importantfinite-state models are often preferred.
lr,10-2-P97-1058,ak
These requirements may be reconciled by using the more complex
<term>
grammar
</term>
to automatically derive a finite-state approximation which can then be used as a filter to guide
<term>
speech recognition
</term>
or to reject many hypotheses at an early stage of
<term>
processing
</term>
.
#29815These requirements may be reconciled by using the more complexgrammar to automatically derive a finite-state approximation which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage of processing.
tech,27-2-P97-1058,ak
These requirements may be reconciled by using the more complex
<term>
grammar
</term>
to automatically derive a finite-state approximation which can then be used as a filter to guide
<term>
speech recognition
</term>
or to reject many hypotheses at an early stage of
<term>
processing
</term>
.
#29832These requirements may be reconciled by using the more complex grammar to automatically derive a finite-state approximation which can then be used as a filter to guidespeech recognition or to reject many hypotheses at an early stage of processing.
tech,39-2-P97-1058,ak
These requirements may be reconciled by using the more complex
<term>
grammar
</term>
to automatically derive a finite-state approximation which can then be used as a filter to guide
<term>
speech recognition
</term>
or to reject many hypotheses at an early stage of
<term>
processing
</term>
.
#29844These requirements may be reconciled by using the more complex grammar to automatically derive a finite-state approximation which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage ofprocessing.
lr,11-3-P97-1058,ak
A method is presented here for calculating such finite-state approximations from
<term>
context-free grammars
</term>
.
#29857A method is presented here for calculating such finite-state approximations fromcontext-free grammars.
tech,6-4-P97-1058,ak
It is essentially different from the
<term>
algorithm
</term>
introduced by Pereira and Wright ( 1991 ; 1996 ) , is faster in some cases , and has the advantage of being open-ended and adaptable .
#29866It is essentially different from thealgorithm introduced by Pereira and Wright (1991; 1996), is faster in some cases, and has the advantage of being open-ended and adaptable.