Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Features that Predict Cue Usage   Our goal is to identify the features that predict the occurrence and
placement of discourse cues in tutorial explanations in order to aid in the
automatic generation of explanations. Previous attempts to devise rules for
text generation were based on intuition or small numbers of constructed
examples. We apply a machine learning program, C4.5, to induce decision trees
for cue occurrence and placement from a corpus of data coded for a variety of
features previously thought to affect cue usage. Our experiments enable us to
identify the features with most predictive power, and show that machine
learning can be used to induce decision trees useful for text generation.

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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
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The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
21
The categories of this paper are: adap-org