Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bootstrapping Structure using Similarity   In this paper a new similarity-based learning algorithm, inspired by string
edit-distance (Wagner and Fischer, 1974), is applied to the problem of
bootstrapping structure from scratch. The algorithm takes a corpus of
unannotated sentences as input and returns a corpus of bracketed sentences. The
method works on pairs of unstructured sentences or sentences partially
bracketed by the algorithm that have one or more words in common. It finds
parts of sentences that are interchangeable (i.e. the parts of the sentences
that are different in both sentences). These parts are taken as possible
constituents of the same type. While this corresponds to the basic
bootstrapping step of the algorithm, further structure may be learned from
comparison with other (similar) sentences.
  We used this method for bootstrapping structure from the flat sentences of
the Penn Treebank ATIS corpus, and compared the resulting structured sentences
to the structured sentences in the ATIS corpus. Similarly, the algorithm was
tested on the OVIS corpus. We obtained 86.04 % non-crossing brackets precision
on the ATIS corpus and 89.39 % non-crossing brackets precision on the OVIS
corpus.

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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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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: nucl-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th