Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Recurrent Neural Networks for Product Attribute Extraction in
  eCommerce   Extracting accurate attribute qualities from product titles is a vital
component in delivering eCommerce customers with a rewarding online shopping
experience via an enriched faceted search. We demonstrate the potential of Deep
Recurrent Networks in this domain, primarily models such as Bidirectional LSTMs
and Bidirectional LSTM-CRF with or without an attention mechanism. These have
improved overall F1 scores, as compared to the previous benchmarks (More et
al.) by at least 0.0391, showcasing an overall precision of 97.94%, recall of
94.12% and the F1 score of 0.9599. This has made us achieve a significant
coverage of important facets or attributes of products which not only shows the
efficacy of deep recurrent models over previous machine learning benchmarks but
also greatly enhances the overall customer experience while shopping online.

27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th