On-Device Information Extraction from SMS using Hybrid Hierarchical Classification

Abstract

Cluttering of SMS inbox is one of the serious problems that users today face in the digital world where every online login, transaction, along with promotions generate multiple SMS. This problem not only prevents users from searching and navigating messages efficiently but often results in users missing out the relevant information associated with the corresponding SMS like offer codes, payment reminders etc. In this paper, we propose a unique architecture to organize and extract the appropriate information from SMS and further display it in an intuitive template. In the proposed architecture, we use a Hybrid Hierarchical Long Short Term Memory (LSTM)-Convolutional Neural Network (CNN) to categorize SMS into multiple classes followed by a set of entity parsers used to extract the relevant information from the classified message. The architecture using its preprocessing techniques not only takes into account the enormous variations observed in SMS data but also makes it efficient for its on-device (mobile phone) functionalities in terms of inference timing and size.

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

“…If not, a custom model is needed, which can be trained and adjusted to the application's needs on a computer or in the cloud. The pretrained model can be a common model used for a specific purpose or tailored to research needs, such as in [21][22][23][24][25][26].…”

On-Device Sentence Similarity for SMS Dataset

“…One of the questions which may arise after discussing about our use case could be Why are we not classifying SMS data into predetermined labels? Classification puts a restriction on the number of classes in which a given dataset can be divided, as can be seen in [1], where the classification of SMS data has been carried out with fixed number of classes. Every time a new label or class needs to be added, we need to retrain the entire model with the new class data.…”