  Twitter is recently being used during crises to communicate with officials
and provide rescue and relief operation in real time. The geographical location
information of the event, as well as users, are vitally important in such
scenarios. The identification of geographic location is one of the challenging
tasks as the location information fields, such as user location and place name
of tweets are not reliable. The extraction of location information from tweet
text is difficult as it contains a lot of non-standard English, grammatical
errors, spelling mistakes, non-standard abbreviations, and so on. This research
aims to extract location words used in the tweet using a Convolutional Neural
Network (CNN) based model. We achieved the exact matching score of 0.929,
Hamming loss of 0.002, and $F_1$-score of 0.96 for the tweets related to the
earthquake. Our model was able to extract even three- to four-word long
location references which is also evident from the exact matching score of over
92\%. The findings of this paper can help in early event localization,
emergency situations, real-time road traffic management, localized
advertisement, and in various location-based services.
