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The high-level contribution of this paper is the development andimplementation of an algorithm to selfextract secondary keywords and theircombinations (combo words) based on abstracts collected using standard primarykeywords for research areas from reputed online digital libraries like IEEEExplore, PubMed Central and etc. Given a collection of N abstracts, wearbitrarily select M abstracts (M<< N; M/N as low as 0.15) and parse each ofthe M abstracts, word by word. Upon the first-time appearance of a word, wequery the user for classifying the word into an Accept-List or non-Accept-List.The effectiveness of the training approach is evaluated by measuring thepercentage of words for which the user is queried for classification when thealgorithm parses through the words of each of the M abstracts. We observed thatas M grows larger, the percentage of words for which the user is queried forclassification reduces drastically. After the list of acceptable words is builtby parsing the M abstracts, we now parse all the N abstracts, word by word, andcount the frequency of appearance of each of the words in Accept-List in theseN abstracts. We also construct a Combo-Accept-List comprising of all possiblecombinations of the single keywords in Accept-List and parse all the Nabstracts, two successive words (combo word) at a time, and count the frequencyof appearance of each of the combo words in the Combo-Accept-List in these Nabstracts.

international journal of computer science and information technology/international journal of computer science and information technology  (chennai. print)

An Algorithm to Self-Extract Secondary Keywords and Their Combinations  Based on Abstracts Collected using Primary Keywords from Online Digital  Libraries