Machine Learning Methods for Keyword Extraction and Indexing
K S Sampada1, N P Kavya2
1K S Sampada, Department of CSE, RNSIT, VTU, Bengaluru (Karnataka), India.
2N P Kavya, Department of CSE, RNSIT, VTU, Bengaluru (Karnataka), India.
Manuscript received on 09 December 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 31 December 2019 | PP: 681-684 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10041292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1004.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The digital age results in the creation of massive information. It is a common tradition among the users to digitalize almost every moment of daily life, since it has become convenient to fetch the information as and when needed from the Internet. User can able to retrieve information by providing query keyword. The objective of the search is to quickly return the set of most relevant documents given a search string. Accomplishing this task for a fixed query involves determining the most relevant documents form the big-data. Queries given to the IR systems are enabled by the keywords. Keyword extraction is a process of identifying the document. Manual keyword extraction is cumbersome and it is in feasible to efficiently identify all the keywords in the document. Therefore the machine learning approaches for keyword extraction are proposed. In this paper various machine learning approaches have discussed along with its merits and de-merits. Here we are also proposing a trained index structure which is efficient to identify the specific locus of the record.
Keywords: Extraction, Indexing, Information System, Machine Learning Approaches.
Scope of the Article: Machine Learning