Information Retrieval Menggunakan Latent Semantic Indexing Pada Ebook

On the Performance of Latent Semantic Indexing based Information Retrieval

Conventional vector-based Information Retrieval (IR) models: Vector Space Model (VSM) and Generalized Vector Space Model (GVSM) represents documents and queries as vectors in a multidimensional space. This high dimensional data places great demands on computing resources. To overcome these problems, Latent Semantic Indexing (LSI), a variant of VSM, projects the documents into a lower dimensiona...

Automatic Cross-Linguistic Information Retrieval using Latent Semantic Indexing

Automatic Cross-Language Information Retrieval using Latent Semantic Indexing

We describe a method for fully automated cross-language document retrieval in which no query translation is required. Queries in one language can retrieve documents in other languages (as well as the original language). This is accomplished by a method that automatically constructs a multi-lingual semantic space using Latent Semantic Indexing (LSI). We present strong preliminary test results fo...

Downdating the Latent Semantic Indexing Model for Conceptual Information Retrieval

Due to the growth of large data collections, information retrieval or database searching is of vital importance. Lexical matching techniques may retrieve irrelevant or inaccurate results because of synonyms and polysemous words, so effective concept-based techniques are needed. One such technique is latent semantic indexing (LSI) which uses a vector-space approach by identifying documents whose...

Comparison of Information Retrieval Techniques: Latent Semantic Indexing and Concept Indexing

The task of information retrieval is to extract relevant documents for a certain query from the collection of documents. As large sets of documents are now increasingly common, there is a growing need for fast and efficient information retrieval algorithms. The algorithms we are dealing with are embedded in the vector space model. In this paper we compare two information retrieval techniques: l...