Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text Segmentation as a Supervised Learning Task   Text segmentation, the task of dividing a document into contiguous segments
based on its semantic structure, is a longstanding challenge in language
understanding. Previous work on text segmentation focused on unsupervised
methods such as clustering or graph search, due to the paucity in labeled data.
In this work, we formulate text segmentation as a supervised learning problem,
and present a large new dataset for text segmentation that is automatically
extracted and labeled from Wikipedia. Moreover, we develop a segmentation model
based on this dataset and show that it generalizes well to unseen natural text.

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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci