What is Latent Semantic Indexing?

Re: What is Latent Semantic Indexing?

Latent semantic indexing is a type of technology that works to understand what a page is about. Latent Semantic Indexing is merely one process within Googles complex ranking algorithm but it can affect your search engine listings considerably. Latent semantic indexing is a search engine algorithm that performs keyword-based analysis to index the web pages. Google realized that it needed a better way for its bots to ascertain the true theme of a webpage and that's what Latent Semantic Indexing is all about. The idea of LSI is to identify the meaning of the information, which words, sentences and documents can be mapped among other website pages. Latent Semantic Indexing is going to change the search engine g**e; you will need to change your seo efforts to pay off big time.

Re: What is Latent Semantic Indexing?

It’s a one kind of search engine method which is used for indexing website.

Re: What is Latent Semantic Indexing?

deemstees;74572 wrote: Can anyone tell me what is LATENT SEMANTIC INDEXING?

As on my knowledge L.S.I is a part of the Google algorithm which aids the search engine in providing the best search results possible.

Re: What is Latent Semantic Indexing?

It's basically an indexing and retrieval method, based on the mathematical technique called SVD. LSI is used to recognize patterns of relationships between concepts and terms. LSI basic principle is that words are used in contexts themselves have a tendency to have the same meanings.

Re: What is Latent Semantic Indexing?

In this post I will try to explain Latent Semantic Indexing (LSI) in simple terms and without the college degree math that is usually required. In a follow up post I will explain why LSI is not used by search engines.

Forget for a moment how search engines like Google rank pages in their search results and let’s take a look at a possible method of indexing and retrieving all the pages relevant to the user’s query before the ranking algorithm is applied.

An obvious method of retrieving relevant pages is by matching the terms of a search query with the same text found in all web pages. However the problem with simple text (lexical) matching methods is that they are inherently inaccurate. This is because there are many ways for a user to express a given concept using different words (synonymy) and also because most words have multiple meanings (polysemy). The problem of synonymy means that the user’s query may not actually match the text on relevant pages so they will be overlooked and the problem of polysemy means that the terms in a user’s query will often match terms in irrelevant pages.

LSI is an attempt to overcome this problem by looking at patterns of word distribution across the whole of the web. In doing so it considers pages that have many words in common to be close in meaning (semantically close) and pages with a few words in common to be semantically distant. The result is an LSI indexed database with similarity values it has calculated for every content word and phrase.

Re: What is Latent Semantic Indexing?

Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur close together in text. A matrix containing word counts per paragraph (rows represent unique words and columns represent each paragraph) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of columns while preserving the similarity structure among rows. Words are then compared by taking the cosine of any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words.
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Mira Informatics