  Every field of research consists of multiple application areas with various
techniques routinely used to solve problems in these wide range of application
areas. With the exponential growth in research volumes, it has become difficult
to keep track of the ever-growing number of application areas as well as the
corresponding problem solving techniques. In this paper, we consider the
computational linguistics domain and present a novel information extraction
system that automatically constructs a pool of all application areas in this
domain and appropriately links them with corresponding problem solving
techniques. Further, we categorize individual research articles based on their
application area and the techniques proposed/used in the article. k-gram based
discounting method along with handwritten rules and bootstrapped pattern
learning is employed to extract application areas. Subsequently, a language
modeling approach is proposed to characterize each article based on its
application area. Similarly, regular expressions and high-scoring noun phrases
are used for the extraction of the problem solving techniques. We propose a
greedy approach to characterize each article based on the techniques. Towards
the end, we present a table representing the most frequent techniques adopted
for a particular application area. Finally, we propose three use cases
presenting an extensive temporal analysis of the usage of techniques and
application areas.
