</term>
over a
<term>
word set based representation
#25899One 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 single words are.
other,40-4-A94-1011,ak
descriptors ( keywords )
</term>
than single
<term>
words
</term>
are . This leads us to consider
#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 single words are.
other,8-5-A94-1011,ak
us to consider the assignment of
<term>
descriptors
</term>
from individual
<term>
phrases
</term>
#25938This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of a word set representation.
other,11-5-A94-1011,ak
descriptors
</term>
from individual
<term>
phrases
</term>
rather than from the
<term>
weighted
#25941This leads us to consider the assignment of descriptors from individual phrases rather than from the weighted sum of a word set representation.
model,25-6-A94-1011,ak
theory about these probably-correct
<term>
rules
</term>
. We then proceed to repeat results
#25979We investigate how sets of individually high-precision rules can result in a low precision when used together, and develop some theory about these probably-correct rules .
other,11-8-A94-1011,ak
sophisticated representations
</term>
of
<term>
documents
</term>
, and lends some support to the
#26030It therefore shows that statistical systems can exploit sophisticated representations of documents , and lends some support to the use of more linguistically sophisticated representations for document classification.