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A Statistical Approach of Keyword Extraction for Efficient Retrieval

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Shruti Luthra, Dinkar Arora, Kanika Mittal, Anusha Chhabra

Shruti Luthra, Dinkar Arora, Kanika Mittal and Anusha Chhabra. A Statistical Approach of Keyword Extraction for Efficient Retrieval. International Journal of Computer Applications 168(7):31-36, June 2017. BibTeX

	author = {Shruti Luthra and Dinkar Arora and Kanika Mittal and Anusha Chhabra},
	title = {A Statistical Approach of Keyword Extraction for Efficient Retrieval},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {7},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {31-36},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017914443},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Large number of techniques for keyword extraction have been proposed for better matching of documents with the user’s query but most of them deal with tf-idf to find the weight age of query terms in the entire document but this can result in improper result as if a term has a low term frequency in overall document but high frequency in a certain part of the document then that term can be ignored by traditional tf-idf method. Through this paper, the keyword extraction is improved using a hybrid technique in which the entire document is split into multiple domains using a master keyword and the frequency of all unique words is found in every domain . The words having high frequency are selected as candidate keywords and the final selection is made on the basis of a graph which is constructed between the keywords using Word Net. The experiments, conducted on various documents show that proposed approach outperforms other keyword extraction methodologies by enhancing document retrieval.


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Information Retrieval, Domain Splitting, Natural Language Processing, Inverse Document Frequency, Word Net