LSI And SEO - LSI Google Algorithms.
Latent Semantic Indexing (LSI) and Relevant Content Are Driving Top Organic Keyword Positions in Google Results.
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About The Latent Semantic Indexing
The Latent Semantic Indexing information retrieval model builds upon the prior research in information retrieval and, using the singular value decomposition (SVD) to reduce the dimensions of the term-document space, attempts to solve the synonomy and polysemy problems that plague automatic information retrieval systems. LSI explicitly represents terms and documents in a rich, high-dimensional space, allowing the underlying ("latent"), semantic relationships between terms and documents to be exploited during searching. Most notably, LSI represents documents in a high-dimensional space or semantic space. Secondly, both terms and documents are explicitly represented in the same space. In many cases no attempt is made to interpret the meaning of each dimension. Each dimension is merely assumed to represent one or more semantic relationships in the term-document space. Finally, because of limits imposed mostly by the computational demands of vector-space approaches to information retrieval, previous attempts focused on relatively small document collections. Keep in mind that Google is the largest collector of stored documents and the Google databases. Google runs on hundreds of thousands of servers-by one estimate, in excess of 450,000-rack servers tied up in thousands of clusters in dozens of data centers around the world. Google has Latent Semantic Indexing LSI algorithmically programmed data centers in Dublin, Ireland; in Virginia; and in California, where it just acquired the million-square-foot headquarters it had been leasing. It recently opened a new center in Atlanta, and is currently building two football-field-sized centers in The Dalles, Ore. Latent Semantic Indexing LSI is able to represent and manipulate large data sets, making it viable for real-d applications. Compared to other information retrieval techniques, LSI performs surprisingly well. LSI relies on the constituent terms of a document to suggest the document's semantic content. However, the LSI model views the terms in a document as somewhat unreliable indicators of the concepts contained in the document. It assumes that the variability of word choice partially obscures the semantic structure of the document. By reducing the dimensionality of the term-document space, the underlying, latent semantic relationships between documents are revealed, and much of the "noise" (differences in word usage, terms that do not help distinguish documents, etc.) is eliminated. Latent Semantic Indexing LSI SEO algorithms statistically analyze the patterns of word usage across the entire document collection, placing documents with similar word usage patterns near each other in the term-document space, and allowing semantically-related documents to be near each other even though they may not share terms. Latent Semantic Indexing LSI and SEO differs from previous attempts at using reduced-space models for information retrieval in several ways. In one test, Dumais found LSI provided more related documents than standard word-based retrieval techniques when searching the standard MED collection. Over five standard document collections, the same study indicated LSI performed an average of better than lexical retrieval techniques. In addition, LSI is fully automatic and easy to use, requiring no complex expressions or syntax to represent the query. Because terms and documents are explicitly represented in the space, relevance feedback can be seamlessly integrated with the LSI model, providing even better overall retrieval performance.
Related Topics: Algorithmically Random Sequence...
Algorithmically random sequence (or random sequence) is an infinite sequence of binary digits that appears random to any algorithm. Latent Semantic Indexing LSI SEO and algorithmically random sequences all apply equally well to sequences on any finite set of characters.
Random sequences are key objects of study in algorithmic information theory and critical to binary-based data retrieval systems such as Google. As different types of algorithms are programmed to pick across multiple variables ranging from algorithms with specific bounds on their running time to algorithms which may ask questions of an oracle, there are different notions of randomness. The most common of random notion within algorithms is known as Martin-L�f randomness (or 1-randomness), but stronger and weaker forms of randomness also exist. The term "random" used to describe Google algorithms often refers to a sequence without clarification that is usually taken to mean "Martin-L�f random". Because infinite sequences of binary digits can be identified with real numbers in the unit interval, random binary sequences are often called random real numbers. Additionally infinite binary sequences correspond to characteristic functions of sets of natural numbers; therefore those sequences might be seen as sets of natural numbers.