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Exploiting Semantic Annotations andQ-Learning for Constructing an Efficient Hierarchy/Graph Texts Organization
2015
The Scientific World Journal
Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the
doi:10.1155/2015/136172
pmid:25685832
pmcid:PMC4313059
fatcat:wqrreslkb5bmdndexweesjuiry