MESSAGENET: MESSAGE CLASSIFICATION USING NATURAL LANGUAGE PROCESSING AND META-DATA

Adar Kahana, Oren Elisha

Keywords: Message classification · Meta data injection · Deep learning · Natural language processing

TL;DR: We propose a deep neural network based on blocks for message classification using meta-data inputs

Abstract:

In this paper we propose a new Deep Learning (DL) approach for message classification. Our method
is based on the state-of-the-art Natural Language Processing (NLP) building blocks, combined
with a novel technique for infusing the meta-data input that is typically available in messages
such as the sender information, timestamps, attached image, audio, affiliations, and more. As we
demonstrate throughout the paper, going beyond the mere text by leveraging all available channels
in the message, could yield an improved representation and higher classification accuracy. To
achieve message representation, each type of input is processed in a dedicated block in the neural
network architecture that is suitable for the data type. Such an implementation enables training all
blocks together simultaneously, and forming cross channels features in the network. We show in the
Experiments Section that in some cases, message’s meta-data holds an additional information that
cannot be extracted just from the text, and when using this information we achieve better performance.
Furthermore, we demonstrate that our multi-modality block approach outperforms other approaches
for injecting the meta data to the the text classifier.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning