  The high-level contribution of this paper is the development and
implementation of an algorithm to selfextract secondary keywords and their
combinations (combo words) based on abstracts collected using standard primary
keywords for research areas from reputed online digital libraries like IEEE
Explore, PubMed Central and etc. Given a collection of N abstracts, we
arbitrarily select M abstracts (M<< N; M/N as low as 0.15) and parse each of
the M abstracts, word by word. Upon the first-time appearance of a word, we
query the user for classifying the word into an Accept-List or non-Accept-List.
The effectiveness of the training approach is evaluated by measuring the
percentage of words for which the user is queried for classification when the
algorithm parses through the words of each of the M abstracts. We observed that
as M grows larger, the percentage of words for which the user is queried for
classification reduces drastically. After the list of acceptable words is built
by parsing the M abstracts, we now parse all the N abstracts, word by word, and
count the frequency of appearance of each of the words in Accept-List in these
N abstracts. We also construct a Combo-Accept-List comprising of all possible
combinations of the single keywords in Accept-List and parse all the N
abstracts, two successive words (combo word) at a time, and count the frequency
of appearance of each of the combo words in the Combo-Accept-List in these N
abstracts.
