On-Device Sentence Similarity for SMS Dataset
This work addresses the problem of determining sentence similarity for SMS data, which is useful for mobile device applications like enhanced searching and navigation, and grouping similar SMS texts.
This paper proposes a pipeline for evaluating text similarity between SMS texts, which are often incomplete and grammatically inconsistent. It uses a Part of Speech model for keyword extraction and statistical methods for similarity comparisons, designed for on-device application.
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.