Frequent Term Based Clustering of Stories with Semantic Analysis for Searching and Retrieval
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Abstract
Effective document organizations are often those which provide a concise representation of text content in a large collection of
documents. We have considered the task of clustering of stories (documents) as a facilitation of effectual document arrangement for searching
and retrieval. We propose a novel representation for a story, based on the essential parts of speech - the nouns, verbs and adjectives. We then
perform a clustering of these story representations, resulting in a graph structure where the story representations are conjoined at nodes having
the same or synonymous noun. Such a structure can be queried for stories by giving a search string. We employ the use of a knowledge bank
throughout the system as a step to realize semantic analysis of the text. For testing the goodness of cluster, we carry out the classification test, on
two data-sets. We are able to achieve significantly high quality of clustering, with promising results in regard to memory compaction.
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Keywords: Document Clustering, Semantic Analysis, Text Mining, Natural Language Processing
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