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HDLTex

HDLTex: Hierarchical Deep Learning for Text Classification

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HDLTex: Hierarchical Deep Learning for Text Classification

Refrenced paper : HDLTex: Hierarchical Deep Learning for Text Classification <https://arxiv.org/abs/1709.08267>__

|Pic|

Documentation:

Increasingly large document collections require improved information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of traditional supervised classifiers has degraded as the number of documents has increased. This is because along with growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.

Installation

Using pip

.. code:: bash

pip install HDLTex

Using git

.. code:: bash

git clone --recursive https://github.com/kk7nc/HDLTex.git

The primary requirements for this package are Python 3 with Tensorflow. The requirements.txt file contains a listing of the required Python packages; to install all requirements, run the following:

.. code:: bash

pip -r install requirements.txt

Or

.. code:: bash

pip3  install -r requirements.txt

Or:

.. code:: bash

conda install --file requirements.txt
    

If the above command does not work, use the following:

.. code:: bash

sudo -H pip  install -r requirements.txt

Datasets for HDLTex:

Linke of dataset: |Data|

Web of Science Dataset WOS-11967 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>__

::

    This dataset contains 11,967 documents with 35 categories which include 7 parents categories.
    

Web of Science Dataset WOS-46985 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>__

::

    This dataset contains 46,985 documents with 134 categories which include 7 parents categories.
  

Web of Science Dataset WOS-5736 <http://dx.doi.org/10.17632/9rw3vkcfy4.2>__

::

    This dataset contains 5,736 documents with 11 categories which include 3 parents categories.

Requirements :

General:

  • Python 3.5 or later see Instruction Documents <https://www.python.org/>__
  • TensorFlow see Instruction Documents <https://www.tensorflow.org/install/install_linux>__.
  • scikit-learn see Instruction Documents <http://scikit-learn.org/stable/install.html>__
  • Keras see Instruction Documents <https://keras.io/>__
  • scipy see Instruction Documents <https://www.scipy.org/install.html>__
  • GPU
    • CUDA® Toolkit 8.0. For details, see NVIDIA’s documentation <https://developer.nvidia.com/cuda-toolkit>__.
    • The NVIDIA drivers associated with CUDA Toolkit 8.0 <http://www.nvidia.com/Download/index.aspx>__.
    • cuDNN v6. For details, see NVIDIA’s documentation <https://developer.nvidia.com/cudnn>__.
    • GPU card with CUDA Compute Capability 3.0 or higher.
    • The libcupti-dev library,
    • To install this library, issue the following command:

::

    $ sudo apt-get install libcupti-dev
    
    

Feature Extraction:

Global Vectors for Word Representation (GLOVE <https://nlp.stanford.edu/projects/glove/>__)

::

    For CNN and RNN you need to download and linked the folder location to GLOVE
    
    

Error and Comments:

Send an email to kk7nc@virginia.edu

Citation:

.. code:: bash

@inproceedings{Kowsari2018HDLTex, 
author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Meimandi, Kiana Jafari and Gerber, Matthew S and Barnes, Laura E},
booktitle={2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)}, 
title={HDLTex: Hierarchical Deep Learning for Text Classification}, 
year={2017},  
pages={364-371}, 
doi={10.1109/ICMLA.2017.0-134},  
month={Dec}
}

.. |DOI| image:: https://img.shields.io/badge/DOI-10.1109%2FICMLA.2017.0--134-blue :target: https://doi.org/10.1109/ICMLA.2017.0-134 .. |travis| image:: https://travis-ci.org/kk7nc/HDLTex.svg?branch=master :target: https://travis-ci.org/kk7nc/HDLTex .. |wercker status| image:: https://app.wercker.com/status/24a123448ba8764b257a1df242146b8e/s/master :target: https://app.wercker.com/project/byKey/24a123448ba8764b257a1df242146b8e .. |Join the chat at https://gitter.im/HDLTex| image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/HDLTex/Lobby?source=orgpage .. |appveyor| image:: https://ci.appveyor.com/api/projects/status/github/kk7nc/HDLTex?branch=master&svg=true :target: https://ci.appveyor.com/project/kk7nc/hdltex .. |arXiv| image:: https://img.shields.io/badge/arXiv-1709.08267-red.svg?style=flat :target: https://arxiv.org/abs/1709.08267 .. |RG| image:: https://img.shields.io/badge/ResearchGate-HDLTex-blue.svg?style=flat :target: https://www.researchgate.net/publication/319968747_HDLTex_Hierarchical_Deep_Learning_for_Text_Classification .. |Binder| image:: https://mybinder.org/badge.svg :target: https://mybinder.org/v2/gh/kk7nc/HDLTex/master .. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592104 :target: https://github.com/kk7nc/HDLTex/blob/master/LICENSE .. |Data| image:: https://img.shields.io/badge/DOI-10.17632/9rw3vkcfy4.6-blue.svg?style=flat :target: http://dx.doi.org/10.17632/9rw3vkcfy4.6 .. |Pic| image:: docs/HDLTex.png :alt: HDLTex as both Hierarchy lavel are DNN .. |twitter| image:: https://img.shields.io/twitter/url/http/shields.io.svg?style=social :target: https://twitter.com/intent/tweet?text=HDLTex:%20Hierarchical%20Deep%20Learning%20for%20Text%20Classification%0aGitHub:&url=https://github.com/kk7nc/HDLTex&hashtags=DeepLearning,Text_Classification,classification,MachineLearning,deep_neural_networks

.. |Download| image:: https://pepy.tech/badge/hdltex :target: https://pepy.tech/project/hdltex

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