#25991We 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.
tech,26-8-A94-1011,ak
sophisticated representations
</term>
for
<term>
document classification
</term>
. This paper reports on work done
#26045It 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.
other,15-4-A94-1011,ak
</term>
of
<term>
documents
</term>
over a
<term>
word set based representation
</term>
of them is that linguistically sophisticated
#25902One of the distinguishing features of a more linguistically sophisticated representation of documents over aword set based representation of them is that linguistically sophisticated units are more frequently individually good predictors of document descriptors (keywords) than single words are.
other,29-3-A94-1011,ak
methods
</term>
to derive notions of
<term>
noun group
</term>
,
<term>
verb group
</term>
, and so
#25859A 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 ofnoun group, verb group, and so on which is inherently extensible to more sophisticated annotation, and does not require a pre-tagged corpus to fit.
tech,16-2-A94-1011,ak
if the power of recently developed
<term>
NLP techniques
</term>
are to be successfully applied in
#25820This perplexing fact needs both an explanation and a solution if the power of recently developedNLP techniques are to be successfully applied in IR.
other,32-3-A94-1011,ak
notions of
<term>
noun group
</term>
,
<term>
verb group
</term>
, and so on which is inherently extensible
#25862A 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 sophisticated annotation, and does not require a pre-tagged corpus to fit.
other,33-4-A94-1011,ak
frequently individually good predictors of
<term>
document descriptors ( keywords )
</term>
than single
<term>
words
</term>
are
#25920One 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 ofdocument descriptors ( keywords ) than single words are.
tech,3-1-A94-1011,ak
practical translation use . The use of
<term>
NLP techniques
</term>
for
<term>
document classification
</term>
#25771The 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).
tech,21-3-A94-1011,ak
POS tagger
</term>
in conjunction with
<term>
unsupervised structure finding methods
</term>
to derive notions of
<term>
noun group
#25851A 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.
tech,45-3-A94-1011,ak
inherently extensible to more sophisticated
<term>
annotation
</term>
, and does not require a
<term>
pre-tagged
#25875A 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.
tech,6-1-A94-1011,ak
use of
<term>
NLP techniques
</term>
for
<term>
document classification
</term>
has not produced significant improvements
#25774The use of NLP techniques fordocument classification has not produced significant improvements in performance within the standard term weighting statistical assignment paradigm (Fagan 1987; Lewis, 1992bc; Buckley, 1993).
other,8-8-A94-1011,ak
statistical systems
</term>
can exploit
<term>
sophisticated representations
</term>
of
<term>
documents
</term>
, and lends
#26027It therefore shows that statistical systems can exploitsophisticated representations of documents, and lends some support to the use of more linguistically sophisticated representations for document classification.
other,40-4-A94-1011,ak
descriptors ( keywords )
</term>
than single
<term>
words
</term>
are . This leads us to consider the
#25927One 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.
measure(ment),13-6-A94-1011,ak
high-precision rules
</term>
can result in a low
<term>
precision
</term>
when used together , and develop
#25967We investigate how sets of individually high-precision rules can result in a lowprecision when used together, and develop some theory about these probably-correct rules.
lr,52-3-A94-1011,ak
annotation
</term>
, and does not require a
<term>
pre-tagged corpus
</term>
to fit . One of the distinguishing
#25882A 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 sophisticated annotation, and does not require apre-tagged corpus to fit.
tech,15-3-A94-1011,ak
is presented which involves using a
<term>
statistical POS tagger
</term>
in conjunction with
<term>
unsupervised
#25845A novel method for adding linguistic annotation to corpora is presented which involves using astatistical 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 sophisticated annotation, and does not require a pre-tagged corpus to fit.
tech,18-1-A94-1011,ak
in performance within the standard
<term>
term weighting statistical assignment paradigm
</term>
( Fagan 1987 ; Lewis , 1992bc ; Buckley
#25786The 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).
other,35-7-A94-1011,ak
significantly improved performance for
<term>
sophisticated representations
</term>
. It therefore shows that
<term>
statistical
#26016We 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 forsophisticated representations.
tech,5-3-A94-1011,ak
</term>
. A novel method for adding
<term>
linguistic annotation
</term>
to
<term>
corpora
</term>
is presented
#25835A novel method for addinglinguistic 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 sophisticated annotation, and does not require a pre-tagged corpus to fit.
other,11-5-A94-1011,ak
<term>
descriptors
</term>
from individual
<term>
phrases
</term>
rather than from the
<term>
weighted
#25941This leads us to consider the assignment of descriptors from individualphrases rather than from the weighted sum of a word set representation.