• Corpus ID: 3183857

Latent Semantic Indexing : An Overview 1 Latent Semantic Indexing : An overview INFOSYS 240 Spring 2000 Final Paper

@inproceedings{Rosario2001LatentSI,
  title={Latent Semantic Indexing : An Overview 1 Latent Semantic Indexing : An overview INFOSYS 240 Spring 2000 Final Paper},
  author={Barbara Rosario},
  year={2001}
}
Typically, information is retrieved by literally matching terms in documents with those of a query. However, lexical matching methods can be inaccurate when they are used to match a user's query. Since there are usually many ways to express a given concept (synonymy), the literal terms in a user's query may not match those of a relevant document. In addition, most words have multiple meanings (polysemy), so terms in a user's query will literally match terms in irrelevant documents. A better… 
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References

SHOWING 1-10 OF 18 REFERENCES
Information Management Tools for Updating an SVD-Encoded Indexing Scheme
TLDR
Latent Semantic Indexing (LSI) is a conceptual indexing technique which uses the SVD to estimate the underlying latent semantic structure of the word to document association, which dampens the effect of word choice variability by representing terms and documents using the (orthogonal) left and right singular vectors.
Cross-Language Information Retrieval Using Latent Semantic Indexing
TLDR
Using the proposed merge strategies, LSI is shown to be able to retrieve relevant documents from either language (Greek or English) without requiring any translation of a user's query.
Latent Semantic Indexing (LSI) and TREC-2
TLDR
LSI is an extension of the vector retrieval method in which the dependencies between terms are explicitly taken into account in the representation and exploited in retrieval by simultaneously modeling all the interrelationships among terms and documents.
Indexing by Latent Semantic Analysis
A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”)
An application of least squares fit mapping to text information retrieval
TLDR
It is discovered that the knowledge about relevance among queries and documents can be used to obtain empirical connections between query terms and the canonical concepts which are used for indexing the content of documents.
Improving text retrieval for the routing problem using latent semantic indexing
TLDR
This paper applies LSI to the routing task, which operates under the assumption that a sample of relevant and non-relevant documents is available to use in constructing the query, and finds that when LSI is used is conjuction with statistical classification, there is a dramatic improvement in performance.
Latent semantic indexing: a probabilistic analysis
TLDR
It is proved that under certain conditions LSI does succeed in capturing the underlying semantics of the corpus and achieves improved retrieval performance.
Information retrieval using a singular value decomposition model of latent semantic structure
In a new method for automatic indexing and retrieval, implicit higher-order structure in the association of terms with documents is modeled to improve estimates of term-document association, and
A similarity-based probability model for latent semantic indexing
  • C. Ding
  • Computer Science
    SIGIR '99
  • 1999
TLDR
A dual probability model is constructed for the Latent Semantic Indexing using the cosine similarity measure, establishing a statistical framework for LSI and leading to a statistical criterion for the optimal semantic dimensions.
Dimensions of meaning
TLDR
The author analyzes the structure of the vector representations and applies them to word sense disambiguation and thesaurus induction and finds that dimensionality reduction by means of a singular value decomposition is employed.
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