Information retrieval in documents using semantic criteria
Bj Mills, S. Venkatesh, Manoj Kumar, Lakshmi Narasimhan
Abstract
Open-access reader
Bj Mills, S. Venkatesh, Manoj Kumar, Lakshmi Narasimhan
Abstract
Open-access reader
\n\t\t\t\t\tThe central problem of automatic retrieval from unformatted text is that computational devices are not adequately trained to look for associated information. However for complete understanding and information retrieval, a complete artificial intelligence would have to be built. This paper describes a method for achieving significant information retrieval by using a semantic search engine. The underlying semantic information is stored in a network of clarified words, linked by logical connections. We employ simple scoring techniques on collections of paths in this network to establish a degree of relevance between a document and a clarified search criterion. This technique has been applied with success to test examples and can be easily scaled up to search large documents.\n\t\t\t\t
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\n\t\t\t\t\tThe central problem of automatic retrieval from unformatted text is that computational devices are not adequately trained to look for associated information. However for complete understanding and information retrieval, a complete artificial intelligence would have to be built. This paper describes a method for achieving significant information retrieval by using a semantic search engine. The underlying semantic information is stored in a network of clarified words, linked by logical connections. We employ simple scoring techniques on collections of paths in this network to establish a degree of relevance between a document and a clarified search criterion. This technique has been applied with success to test examples and can be easily scaled up to search large documents.\n\t\t\t\t
Key concepts: Information retrieval, Computer science, Relevance (law), Concept search, Explicit semantic analysis, Document retrieval, Human–computer information retrieval, Semantic search