In this paper we present a survey of various techniques available in text mining for keyword and keyphrase extraction. Keywords and keyphrases are very useful in analyzing large amount of textual material quickly and efficiently search over the internet besides being useful for many other purposes. Keywords and keyphrases are set of representative words of a document that give high-level specification of the content for interested readers. They are used highly in the field of Computer Science especially in Information Retrieval and Natural Language Processing and can be used for index generation, query refinement, text summarization, author assistance, etc. We have also discussed some important feature selection metrics generally employed by researchers to rank candidate keywords and keyphrases according to their importance.
In this paper we propose an unsupervised, domain independent as well as corpus independent approach for automatic keyword extraction. In second part of the paper we have suggested an extension of the approach to extract keyphrases from the document. The approach is general and can be applied to any language. However, we have tested the approach on Hindi language. Our approach combines the information contained in frequency and spatial distribution of a word in order to extract keywords from a document. Our work is especially significant in the light that it has been implemented and tested on Hindi which is a resource poor and underrepresented language.
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