Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving average ranking precision in user searches for biomedical
  research datasets   Availability of research datasets is keystone for health and life science
study reproducibility and scientific progress. Due to the heterogeneity and
complexity of these data, a main challenge to be overcome by research data
management systems is to provide users with the best answers for their search
queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we
investigate a novel ranking pipeline to improve the search of datasets used in
biomedical experiments. Our system comprises a query expansion model based on
word embeddings, a similarity measure algorithm that takes into consideration
the relevance of the query terms, and a dataset categorisation method that
boosts the rank of datasets matching query constraints. The system was
evaluated using a corpus with 800k datasets and 21 annotated user queries. Our
system provides competitive results when compared to the other challenge
participants. In the official run, it achieved the highest infAP among the
participants, being +22.3% higher than the median infAP of the participant's
best submissions. Overall, it is ranked at top 2 if an aggregated metric using
the best official measures per participant is considered. The query expansion
method showed positive impact on the system's performance increasing our
baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively.
Our similarity measure algorithm seems to be robust, in particular compared to
Divergence From Randomness framework, having smaller performance variations
under different training conditions. Finally, the result categorization did not
have significant impact on the system's performance. We believe that our
solution could be used to enhance biomedical dataset management systems. In
particular, the use of data driven query expansion methods could be an
alternative to the complexity of biomedical terminologies.

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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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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: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin