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\documentclass[11pt,a4paper]{article}
\usepackage[hyperref]{emnlp-ijcnlp-2019}
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\usepackage{url}

\aclfinalcopy % Uncomment this line for the final submission

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\newcommand\BibTeX{B{\sc ib}\TeX}
\newcommand\confname{EMNLP-IJCNLP 2019}
\newcommand\conforg{SIGDAT}

\title{A Label Informative Wide \& Deep Classifier for Patents and Papers}

\author{Muyao Niu \\
  Patsnap Ltd. \\
  \texttt{niumuyao@patsnap.com} \\\And
  Jie Cai \\
  Patsnap Ltd. \\
  \texttt{caijie@patsnap.com} \\}

\date{}

\begin{document}
\maketitle
\begin{abstract}
In this paper, we aim at classifying both patents and papers to the well-established Cooperative Patent Classification (CPC). We propose a label-informative classifier based on the Wide \& Deep structure, where the Wide part encodes string-level similarities between texts and labels,  and the Deep part captures semantic-level similarities via non-linear transformations. Our model trains on millions of patents, and transfers to papers by developing distant-supervised training set and domain-specific features. Extensive experiments show that our model achieves comparable performance to the state-of-the-art model used in industry on both patents and papers. The output of this work should facilitate the searching, granting and filing of innovative ideas for patent examiners, attorneys and researchers.

%In this paper, we deal with the classification problem on scientific publications (i.e. patents and papers) where the discriminative label descriptions are available. We adopt Wide \& Deep framework, where we implement retrieval features in the Wide part to precisely capture the label cues in texts. The proposed way of integrating the label side information is shown to be more effective than previous label embedding based methods. Our model trains on millions of patents, and transfers to paper dataset by adding domain-specific features. We finally provide the state-of-the-art performance on classifying both patents and papers into the well-established Cooperative Patent Classification (CPC) with more than 600 labels. The results of this work should help to reduce the immense manual work of patent examiners, and to reduce the gap between patents and papers in the same classification scheme.
\end{abstract}

\input{intro}

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%\section*{Acknowledgments}

%\noindent \textbf{Preparing References:} \\
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\end{document}
