Extending Information Retrieval by Adjusting Text Feature Vectors

Automatic detection of text scope is now crucial for information retrieval tasks owing to semantic, linguistic, and unexpressive content problems, which has increased the demand for uncomplicated, language-independent, and scope-based strategies. In this paper, we extend the vector of documents with exerting impressive words to simplify expressiveness of each document from extracted essential words of related documents and then analyze the network of these words to detect words that share meaningful concepts related to exactly our document. In other words, we analyze each document in only one topic: the topic of that document. We changed measures of social network analysis according to weights of the document words. The impression of these new words to the document can be exerted as changing the document vector weights or inserting these words as metadata to the document. As an example, we classified documents and compared effectiveness of our Intelligent Information Retrieval (IIR) model.

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