  Determining the sentence similarity between Short Message Service (SMS)
texts/sentences plays a significant role in mobile device industry. Gauging the
similarity between SMS data is thus necessary for various applications like
enhanced searching and navigation, clubbing together SMS of similar type when
given a custom label or tag is provided by user irrespective of their sender
etc. The problem faced with SMS data is its incomplete structure and
grammatical inconsistencies. In this paper, we propose a unique pipeline for
evaluating the text similarity between SMS texts. We use Part of Speech (POS)
model for keyword extraction by taking advantage of the partial structure
embedded in SMS texts and similarity comparisons are carried out using
statistical methods. The proposed pipeline deals with major semantic variations
across SMS data as well as makes it effective for its application on-device
(mobile phone). To showcase the capabilities of our work, our pipeline has been
designed with an inclination towards one of the possible applications of SMS
text similarity discussed in one of the following sections but nonetheless
guarantees scalability for other applications as well.
