other,32-3-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 .
|
#19977
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 sophisticated annotation, and does not require a pre-tagged corpus to fit. |
tech,18-9-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>
.
|
#20181
This paper reports on work done for the LRE project SmTA double check, which is creating aPC based tool to be used in the technical abstracting industry. |
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. |
other,20-5-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>
.
|
#20065
This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of aword set representation. |
other,15-4-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 .
|
#20017
One 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. |
lr,52-3-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 .
|
#19997
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 sophisticated annotation, and does not require apre-tagged corpus to fit. |
tech,26-7-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>
.
|
#20122
We then proceed to repeat results which show that standard statistical models are not particularly suitable for exploiting linguistically sophisticated representations, and show that astatistically fitted rule-based model provides significantly improved performance for sophisticated representations. |
tech,15-3-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 .
|
#19960
A 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. |
other,5-3-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 .
|
#19950
A 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. |
tech,16-2-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>
.
|
#19935
This perplexing fact needs both an explanation and a solution if the power of recently developedNLP techniques are to be successfully applied in IR. |
other,8-8-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>
.
|
#20142
It 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,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. |
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,6-1-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 ) .
|
#19889
The 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,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. |
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,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. |
other,8-4-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 .
|
#20010
One of the distinguishing features of a morelinguistically 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 single words are. |
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,8-5-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. |