  With the overwhelming transition to smart phones, storing important
information in the form of unstructured text has become habitual to users of
mobile devices. From grocery lists to drafts of emails and important speeches,
users store a lot of data in the form of unstructured text (for eg: in the
Notes application) on their devices, leading to cluttering of data. This not
only prevents users from efficient navigation in the applications but also
precludes them from perceiving the relations that could be present across data
in those applications. This paper proposes a novel pipeline to generate a set
of tags using world knowledge based on the keywords and concepts present in
unstructured textual data. These tags can then be used to summarize, categorize
or search for the desired information thus enhancing user experience by
allowing them to have a holistic outlook of the kind of information stored in
the form of unstructured text. In the proposed system, we use an on-device
(mobile phone) efficient CNN model with pruned ConceptNet resource to achieve
our goal. The architecture also presents a novel ranking algorithm to extract
the top n tags from any given text.

Computation and Language