Abstract:
Eradicating terrorism using state of the art technologies has proved to be an active area of
research with the increase in the incidents involving terrorism. It has been observed that the
terrorism incident data available has played an important role in devising the techniques to help
curb the terrorism. Therefore, the importance of acquiring the terrorist incident data is very crucial
part. The gathered data gives researcher the ability to identify and link the patterns used by terrorist
through the analysis of gathered data. This is done using different Machine learning algorithms
and therefore helps in the prevention of future terrorist activities. However, due to the unavailability of an automated system the terrorism incident data collector has to perform a very
lengthy task of going through different news articles to gather data and also has to verify the
accuracy of the data. This makes it very hard to update the database in time. In this research, we
have taken the advantage of Natural Language Processing and specifically Named Entity
Recognition to obtain entities from news articles containing reports of terrorist incidents. We have
specifically trained the opensource library of Python named as Spacy to perform this task, which
used Convolutional Neural Network Classifier to train identify entities in a text. Presently, no
approach is powerful enough to create a fully automated database creation process with 100%
accuracy, but our approach significantly reduces the overhead of the terrorist incidents data
collection process. This will ultimately lead to a more convenient and fast way of terrorism
incidents collection of data.
