Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Categorizing Items with Short and Noisy Descriptions using Ensembled
  Transferred Embeddings   Item categorization is a machine learning task which aims at classifying
e-commerce items, typically represented by textual attributes, to their most
suitable category from a predefined set of categories. An accurate item
categorization system is essential for improving both the user experience and
the operational processes of the company. In this work, we focus on item
categorization settings in which the textual attributes representing items are
noisy and short, and labels (i.e., accurate classification of items into
categories) are not available. In order to cope with such settings, we propose
a novel learning framework, Ensembled Transferred Embeddings (ETE), which
relies on two key ideas: 1) labeling a relatively small sample of the target
dataset, in a semi-automatic process, and 2) leveraging other datasets from
related domains or related tasks that are large-scale and labeled, to extract
"transferable embeddings". Evaluation of ETE on a large-scale real-world
dataset provided to us by PayPal, shows that it significantly outperforms
traditional as well as state-of-the-art item categorization methods.

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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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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: quant-ph
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The categories of this paper are: supr-con
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph