Identifying Keywords and Key Phrases
Ms Ashiwini.c.Madane, BE .MTech (pursing), BharatiVidyappeth University, College of Engineering, Pune – 43.
Manuscript received on July 01, 2012. | Revised Manuscript received on July 04, 2012. | Manuscript published on July 05, 2012. | PP: 142-143 | Volume-2, Issue-3, July 2012. | Retrieval Number: C0723052312/2012©BEIESP
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© The Authors. Published By: 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: Keywords and key phrases are widely used in large document collections. They describe the content of single documents and provide a kind of semantic metadata that is useful for a variety of purposes. Text mining is powerful tool to find useful and needed information from huge data set. For context based text mining, key phrases are used. Key phrases provide brief summary about the contents of documents. In document clustering, number of total cluster is not known in advance. In Kmeans, if prespecified number of clusters modified, the precision of each result is also modified. Therefore Kea, is algorithm for automatically extracting key phrases from text is used. In this kea algorithm, number of clusters is automatically determined by using extracted key phrases. Keameans clustering algorithm provide easy and efficient way to extract test document from large quantity of resources. Key phrase play important role in text indexing, summarization and categorization. Key phrases are selected manually. Assigning key phrases manually is tedious process that requires knowledge of subject. Therefore automatic extraction techniques are most useful
Keywords: Text mining, Key phrase extraction, key phrase.