other,6-2-P03-1068,bq |
backbone of the
<term>
annotation
</term>
are
<term>
|
semantic
|
roles
</term>
in the
<term>
frame semantics
|
#4969
The backbone of the annotation aresemantic roles in the frame semantics paradigm. |
other,13-1-P06-1052,bq |
problem
</term>
: Given an
<term>
underspecified
|
semantic
|
representation ( USR )
</term>
of a
<term>
|
#11132
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 equivalent readings. |
other,6-1-T78-1031,bq |
performing
<term>
inference
</term>
in
<term>
|
semantic
|
networks
</term>
are presented and compared
|
#12056
Two styles of performing inference insemantic networks are presented and compared. |
other,25-3-C88-1007,bq |
obviating the need for answers to
<term>
|
semantic
|
questions
</term>
that we do not yet have
|
#15088
The principle advantage of this approach is that knowledge concerning translation equivalence of expressions may be directly exploited, obviating the need for answers tosemantic questions that we do not yet have. |
other,17-6-A94-1026,bq |
component places some restrictions on the
<term>
|
semantic
|
categories
</term>
of the
<term>
adjoining
|
#20477
The basic idea of this method is that a compound noun component places some restrictions on thesemantic categories of the adjoining words. |
other,31-1-C90-3045,bq |
</term>
, for instance , to the task of
<term>
|
semantic
|
interpretation
</term>
or
<term>
automatic
|
#16456
The unique properties of tree-adjoining grammars (TAG) present a challenge for the application of TAGs beyond the limited confines of syntax, for instance, to the task ofsemantic interpretation or automatic translation of natural language. |
other,3-1-C90-3063,bq |
defeasibility
</term>
. Manual acquisition of
<term>
|
semantic
|
constraints
</term>
in broad domains is very
|
#16607
Manual acquisition ofsemantic constraints in broad domains is very expensive. |
measure(ment),19-3-H01-1058,bq |
performance
</term>
( typically ,
<term>
word or
|
semantic
|
error rate
</term>
) from a list of
<term>
|
#1091
The oracle knows the reference word string and selects the word string with the best performance (typically, word or semantic error rate) from a list of word strings, where each word string has been obtained by using a different LM. |
other,6-3-C94-1052,bq |
experiments , corresponding to rather
<term>
closed
|
semantic
|
domains
</term>
, have been developed in
|
#20796
Several experiments, corresponding to rather closed semantic domains, have been developed in order to generate lexical cross-relations between English and Spanish. |
other,25-2-I05-5003,bq |
<term>
classifiers
</term>
to predict
<term>
|
semantic
|
equivalence
</term>
and
<term>
entailment
</term>
|
#8362
This paper investigates the utility of applying standard MT evaluation methods (BLEU, NIST, WER and PER) to building classifiers to predictsemantic equivalence and entailment. |
other,10-1-P03-1009,bq |
<term>
clustering
</term>
in inducing
<term>
|
semantic
|
verb classes
</term>
from undisambiguated
|
#3894
Previous research has demonstrated the utility of clustering in inducingsemantic verb classes from undisambiguated corpus data. |
other,2-2-H92-1026,bq |
</term>
incorporates
<term>
lexical , syntactic ,
|
semantic
|
, and structural information
</term>
from
|
#18925
HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. |
other,18-6-C90-3063,bq |
statistics
</term>
indeed reflect the
<term>
|
semantic
|
constraints
</term>
and thus provide a basis
|
#16709
The results of the experiment show that in most of the cases the cooccurrence statistics indeed reflect thesemantic constraints and thus provide a basis for a useful disambiguation tool. |
other,20-2-N03-1012,bq |
hypotheses ( SRH )
</term>
in terms of their
<term>
|
semantic
|
coherence
</term>
. We conducted an
<term>
|
#2476
We apply our system to the task of scoring alternative speech recognition hypotheses (SRH) in terms of theirsemantic coherence. |
tech,25-2-H01-1041,bq |
meaning representation
</term>
called a
<term>
|
semantic
|
frame
</term>
. The key features of the
<term>
|
#436
The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called asemantic frame. |
other,39-3-H92-1060,bq |
gluing them together to obtain a single
<term>
|
semantic
|
frame
</term>
encoding the full
<term>
meaning
|
#19425
Robust parsing is applied only after a full analysis has failed, and it involves the two stages of 1) parsing a set of phrases and clauses, and 2) gluing them together to obtain a singlesemantic frame encoding the full meaning of the sentence. |
other,26-7-A94-1026,bq |
its neighbors is not a member of the
<term>
|
semantic
|
set
</term>
defined by the
<term>
homophone
|
#20510
The method accurately determines that a homophone is misused in a compound noun if one or both of its neighbors is not a member of thesemantic set defined by the homophone. |
other,8-3-C90-3063,bq |
these
<term>
statistics
</term>
reflect
<term>
|
semantic
|
constraints
</term>
and thus are used to
|
#16641
To a large extent, these statistics reflectsemantic constraints and thus are used to disambiguate anaphora references and syntactic ambiguities. |
other,1-4-P82-1035,bq |
being described . These
<term>
syntactic and
|
semantic
|
expectations
</term>
can be used to figure
|
#13058
These syntactic and semantic expectations can be used to figure out unknown words from context, constrain the possible word-senses of words with multiple meanings (ambiguity), fill in missing words (elllpsis), and resolve referents (anaphora). |
tech,16-1-I05-5003,bq |
related to the task of
<term>
sentence-level
|
semantic
|
equivalence classification
</term>
. This
|
#8333
The task of machine translation (MT) evaluation is closely related to the task of sentence-level semantic equivalence classification. |