tech,24-2-A94-1011,bq |
This perplexing fact needs both an explanation and a solution if the power of recently developed
<term>
NLP techniques
</term>
are to be successfully applied in
<term>
IR
</term>
.
|
#19943
This perplexing fact needs both an explanation and a solution if the power of recently developed NLP techniques are to be successfully applied inIR. |
other,36-7-A94-1011,bq |
We then proceed to repeat results which show that standard
<term>
statistical models
</term>
are not particularly suitable for exploiting
<term>
linguistically sophisticated representations
</term>
, and show that a
<term>
statistically fitted rule-based model
</term>
provides significantly improved performance for sophisticated
<term>
representations
</term>
.
|
#20132
We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticatedrepresentations. |
other,45-3-A94-1011,bq |
A novel method for adding
<term>
linguistic annotation
</term>
to
<term>
corpora
</term>
is presented which involves using a
<term>
statistical POS tagger
</term>
in conjunction with
<term>
unsupervised structure finding methods
</term>
to derive notions of
<term>
noun group
</term>
,
<term>
verb group
</term>
, and so on which is inherently extensible to more sophisticated
<term>
annotation
</term>
, and does not require a
<term>
pre-tagged corpus
</term>
to fit .
|
#19990
A novel method for adding linguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction with unsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticatedannotation, and does not require a pre-tagged corpus to fit. |
other,23-4-A94-1011,bq |
One of the distinguishing features of a more
<term>
linguistically sophisticated representation of documents
</term>
over a
<term>
word set based representation
</term>
of them is that
<term>
linguistically sophisticated units
</term>
are more frequently individually good predictors of
<term>
document descriptors ( keywords )
</term>
than single
<term>
words
</term>
are .
|
#20025
One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is thatlinguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than single words are. |
other,25-6-A94-1011,bq |
We investigate how sets of individually high-precision
<term>
rules
</term>
can result in a
<term>
low precision
</term>
when used together , and develop some theory about these probably-correct
<term>
rules
</term>
.
|
#20094
We investigate how sets of individually high-precision rules can result in a low precision when used together, and develop some theory about these probably-correctrules. |
other,26-9-A94-1011,bq |
This paper reports on work done for the
<term>
LRE project SmTA double check
</term>
, which is creating a
<term>
PC based tool
</term>
to be used in the
<term>
technical abstracting industry
</term>
.
|
#20189
This paper reports on work done for the LRE project SmTA double check, which is creating a PC based tool to be used in thetechnical abstracting industry. |
other,40-4-A94-1011,bq |
One of the distinguishing features of a more
<term>
linguistically sophisticated representation of documents
</term>
over a
<term>
word set based representation
</term>
of them is that
<term>
linguistically sophisticated units
</term>
are more frequently individually good predictors of
<term>
document descriptors ( keywords )
</term>
than single
<term>
words
</term>
are .
|
#20042
One of the distinguishing features of a more linguistically sophisticated representation of documents over a word set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than singlewords are. |
tech,4-8-A94-1011,bq |
It therefore shows that
<term>
statistical systems
</term>
can exploit
<term>
sophisticated representations of documents
</term>
, and lends some support to the use of more
<term>
linguistically sophisticated representations
</term>
for
<term>
document classification
</term>
.
|
#20138
It therefore shows thatstatistical systems can exploit sophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations for document classification. |
other,18-7-A94-1011,bq |
We then proceed to repeat results which show that standard
<term>
statistical models
</term>
are not particularly suitable for exploiting
<term>
linguistically sophisticated representations
</term>
, and show that a
<term>
statistically fitted rule-based model
</term>
provides significantly improved performance for sophisticated
<term>
representations
</term>
.
|
#20114
We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploitinglinguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticated representations. |
other,22-8-A94-1011,bq |
It therefore shows that
<term>
statistical systems
</term>
can exploit
<term>
sophisticated representations of documents
</term>
, and lends some support to the use of more
<term>
linguistically sophisticated representations
</term>
for
<term>
document classification
</term>
.
|
#20156
It therefore shows that statistical systems can exploit sophisticated representations of documents, and lends some support to the use of morelinguistically sophisticated representations for document classification. |
other,8-9-A94-1011,bq |
This paper reports on work done for the
<term>
LRE project SmTA double check
</term>
, which is creating a
<term>
PC based tool
</term>
to be used in the
<term>
technical abstracting industry
</term>
.
|
#20171
This paper reports on work done for theLRE project SmTA double check, which is creating a PC based tool to be used in the technical abstracting industry. |
other,7-6-A94-1011,bq |
We investigate how sets of individually high-precision
<term>
rules
</term>
can result in a
<term>
low precision
</term>
when used together , and develop some theory about these probably-correct
<term>
rules
</term>
.
|
#20076
We investigate how sets of individually high-precisionrules can result in a low precision when used together, and develop some theory about these probably-correct rules. |
other,11-5-A94-1011,bq |
This leads us to consider the assignment of
<term>
descriptors
</term>
from individual
<term>
phrases
</term>
rather than from the
<term>
weighted sum
</term>
of a
<term>
word set representation
</term>
.
|
#20056
This leads us to consider the assignment of descriptors from individualphrases rather than from the weighted sum of a word set representation. |
tech,18-1-A94-1011,bq |
The use of
<term>
NLP techniques
</term>
for
<term>
document classification
</term>
has not produced significant improvements in performance within the standard
<term>
term weighting statistical assignment paradigm
</term>
( Fagan 1987 ; Lewis , 1992bc ; Buckley , 1993 ) .
|
#19901
The use of NLP techniques for document classification has not produced significant improvements in performance within the standardterm weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993). |
tech,3-1-A94-1011,bq |
The use of
<term>
NLP techniques
</term>
for
<term>
document classification
</term>
has not produced significant improvements in performance within the standard
<term>
term weighting statistical assignment paradigm
</term>
( Fagan 1987 ; Lewis , 1992bc ; Buckley , 1993 ) .
|
#19886
The use ofNLP techniques for document classification has not produced significant improvements in performance within the standard term weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993). |
measure(ment),12-6-A94-1011,bq |
We investigate how sets of individually high-precision
<term>
rules
</term>
can result in a
<term>
low precision
</term>
when used together , and develop some theory about these probably-correct
<term>
rules
</term>
.
|
#20081
We investigate how sets of individually high-precision rules can result in alow precision when used together, and develop some theory about these probably-correct rules. |
tech,10-7-A94-1011,bq |
We then proceed to repeat results which show that standard
<term>
statistical models
</term>
are not particularly suitable for exploiting
<term>
linguistically sophisticated representations
</term>
, and show that a
<term>
statistically fitted rule-based model
</term>
provides significantly improved performance for sophisticated
<term>
representations
</term>
.
|
#20106
We then proceed to repeat results which show that standardstatistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that a statistically fitted rule-based model provides significantly improved performance for sophisticated representations. |
other,8-5-A94-1011,bq |
This leads us to consider the assignment of
<term>
descriptors
</term>
from individual
<term>
phrases
</term>
rather than from the
<term>
weighted sum
</term>
of a
<term>
word set representation
</term>
.
|
#20053
This leads us to consider the assignment ofdescriptors from individual phrases rather than from the weighted sum of a word set representation. |
tech,26-8-A94-1011,bq |
It therefore shows that
<term>
statistical systems
</term>
can exploit
<term>
sophisticated representations of documents
</term>
, and lends some support to the use of more
<term>
linguistically sophisticated representations
</term>
for
<term>
document classification
</term>
.
|
#20160
It therefore shows that statistical systems can exploit sophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations fordocument classification. |
tech,21-3-A94-1011,bq |
A novel method for adding
<term>
linguistic annotation
</term>
to
<term>
corpora
</term>
is presented which involves using a
<term>
statistical POS tagger
</term>
in conjunction with
<term>
unsupervised structure finding methods
</term>
to derive notions of
<term>
noun group
</term>
,
<term>
verb group
</term>
, and so on which is inherently extensible to more sophisticated
<term>
annotation
</term>
, and does not require a
<term>
pre-tagged corpus
</term>
to fit .
|
#19966
A novel method for adding linguistic annotation to corpora is presented which involves using a statistical POS tagger in conjunction withunsupervised structure finding methods to derive notions of noun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit. |