Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Effective Sentence Simplification for Automatic Processing of
  Biomedical Text   The complexity of sentences characteristic to biomedical articles poses a
challenge to natural language parsers, which are typically trained on
large-scale corpora of non-technical text. We propose a text simplification
process, bioSimplify, that seeks to reduce the complexity of sentences in
biomedical abstracts in order to improve the performance of syntactic parsers
on the processed sentences. Syntactic parsing is typically one of the first
steps in a text mining pipeline. Thus, any improvement in performance would
have a ripple effect over all processing steps. We evaluated our method using a
corpus of biomedical sentences annotated with syntactic links. Our empirical
results show an improvement of 2.90% for the Charniak-McClosky parser and of
4.23% for the Link Grammar parser when processing simplified sentences rather
than the original sentences in the corpus.

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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph