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LSI helps overcome synonymy by increasing recall , one of the most problematic constraints of Boolean keyword queries and vector space models. LSI is also used to perform automated document categorization. In fact, several experiments have demonstrated that there are a number of correlations between the way LSI and humans process and categorize text. During categorization processing, the concepts contained in the documents being categorized are compared to the concepts contained in the example items, and a category or categories is assigned to the documents based on the similarities between the concepts they contain and the concepts that are contained in the example documents.

Dynamic clustering based on the conceptual content of documents can also be accomplished using LSI.

Vector Representations of Words

Clustering is a way to group documents based on their conceptual similarity to each other without using example documents to establish the conceptual basis for each cluster. This is very useful when dealing with an unknown collection of unstructured text. Because it uses a strictly mathematical approach, LSI is inherently independent of language.

This enables LSI to elicit the semantic content of information written in any language without requiring the use of auxiliary structures, such as dictionaries and thesauri. LSI can also perform cross-linguistic concept searching and example-based categorization.

For example, queries can be made in one language, such as English, and conceptually similar results will be returned even if they are composed of an entirely different language or of multiple languages. LSI is not restricted to working only with words. It can also process arbitrary character strings.


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Any object that can be expressed as text can be represented in an LSI vector space. LSI automatically adapts to new and changing terminology, and has been shown to be very tolerant of noise i. LSI also deals effectively with sparse, ambiguous, and contradictory data. Text does not need to be in sentence form for LSI to be effective.

It can work with lists, free-form notes, email, Web-based content, etc. As long as a collection of text contains multiple terms, LSI can be used to identify patterns in the relationships between the important terms and concepts contained in the text. LSI has proven to be a useful solution to a number of conceptual matching problems.

LSI uses common linear algebra techniques to learn the conceptual correlations in a collection of text. In general, the process involves constructing a weighted term-document matrix, performing a Singular Value Decomposition on the matrix, and using the matrix to identify the concepts contained in the text. This matrix is usually very large and very sparse.

Once a term-document matrix is constructed, local and global weighting functions can be applied to it to condition the data. Some common local weighting functions [30] are defined in the following table. Empirical studies with LSI report that the Log and Entropy weighting functions work well, in practice, with many data sets. A rank-reduced, singular value decomposition is performed on the matrix to determine patterns in the relationships between the terms and concepts contained in the text.

Reducing the Dimensionality of Vector Space Embeddings of Graphs

In the formula, A is the supplied m by n weighted matrix of term frequencies in a collection of text where m is the number of unique terms, and n is the number of documents. S is a computed r by r diagonal matrix of decreasing singular values, and D is a computed n by r matrix of document vectors. This effectively reduces the term and document vector matrix sizes to m by k and n by k respectively.

The SVD operation, along with this reduction, has the effect of preserving the most important semantic information in the text while reducing noise and other undesirable artifacts of the original space of A. This reduced set of matrices is often denoted with a modified formula such as:. Efficient LSI algorithms only compute the first k singular values and term and document vectors as opposed to computing a full SVD and then truncating it. PCA loses the sparseness of the A matrix, which can make it infeasible for large lexicons. The computed T k and D k matrices define the term and document vector spaces, which with the computed singular values, S k , embody the conceptual information derived from the document collection.

Graph classification and clustering based on vector space embedding in SearchWorks catalog

The similarity of terms or documents within these spaces is a factor of how close they are to each other in these spaces, typically computed as a function of the angle between the corresponding vectors. The same steps are used to locate the vectors representing the text of queries and new documents within the document space of an existing LSI index. The new column in A is computed using the originally derived global term weights and applying the same local weighting function to the terms in the query or in the new document. A drawback to computing vectors in this way, when adding new searchable documents, is that terms that were not known during the SVD phase for the original index are ignored.

These terms will have no impact on the global weights and learned correlations derived from the original collection of text.

However, the computed vectors for the new text are still very relevant for similarity comparisons with all other document vectors. The process of augmenting the document vector spaces for an LSI index with new documents in this manner is called folding in. Although the folding-in process does not account for the new semantic content of the new text, adding a substantial number of documents in this way will still provide good results for queries as long as the terms and concepts they contain are well represented within the LSI index to which they are being added.

When the terms and concepts of a new set of documents need to be included in an LSI index, either the term-document matrix, and the SVD, must be recomputed or an incremental update method such as the one described in [13] is needed. It is generally acknowledged that the ability to work with text on a semantic basis is essential to modern information retrieval systems. As a result, the use of LSI has significantly expanded in recent years as earlier challenges in scalability and performance have been overcome.

LSI is being used in a variety of information retrieval and text processing applications, although its primary application has been for concept searching and automated document categorization. LSI is increasingly being used for electronic document discovery eDiscovery to help enterprises prepare for litigation. In eDiscovery, the ability to cluster, categorize, and search large collections of unstructured text on a conceptual basis is essential. Concept-based searching using LSI has been applied to the eDiscovery process by leading providers as early as Early challenges to LSI focused on scalability and performance.

LSI requires relatively high computational performance and memory in comparison to other information retrieval techniques. Real-world applications involving more than 30 million documents that were fully processed through the matrix and SVD computations are common in some LSI applications. A fully scalable unlimited number of documents, online training implementation of LSI is contained in the open source gensim software package. Another challenge to LSI has been the alleged difficulty in determining the optimal number of dimensions to use for performing the SVD.

As a general rule, fewer dimensions allow for broader comparisons of the concepts contained in a collection of text, while a higher number of dimensions enable more specific or more relevant comparisons of concepts. The actual number of dimensions that can be used is limited by the number of documents in the collection.

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Research has demonstrated that around dimensions will usually provide the best results with moderate-sized document collections hundreds of thousands of documents and perhaps dimensions for larger document collections millions of documents. Using a synonym test or prediction of missing words are two possible methods to find the correct dimensionality.

From Wikipedia, the free encyclopedia. Lexical lexis lexicology. Statistical Structural. Prototype theory Force dynamics. Latent Machine-learning. Semantic Web Semantic wiki. Dumais Annual Review of Information Science and Technology. Behavior Research Methods. Journal of Computational Science. Consciousness and Cognition.

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Elman October Biotechnology Law Report. Howard; Michael J. Kahana Zaromb; et al. Retrieved May 8, Archived from the original PDF on Linear Algebra and its Applications. Journal of the American Society for Information Science.