Improving information retrieval using latent semantic indexing

By S. Deerwester, Susan T. Dumais, T. Landauer, G. W. Fumas, L. L. Beck. 1988

Subfields: Information Retrieval & Search

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Citations in the corpus

Experience Grounds Language (2020, background): “However, the most successful vector representations came from Latent Semantic Indexing/Analysis (Deerwester et al., 1988, 1990; Du-mais, 2004), which represents words by their co-occurrence with other words.”
Label-Embedding for Image Classification (2015, neutral): “Standard approaches to produce word embeddings from co-ocurrences include Latent Semantic Analyis (LSA) [12], probabilistic Latent Semantic Analysis (pLSA) [24] or Latent Dirichlet Allocation (LDA) [6].”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, methodology): “LSI: Latent Semantic Indexing ( LSI) [12], also called latent semantic analysis (LSA), is an indexing and retrieval method that can identify the relationship between the terms and concepts contained in an unstructured collection of text by using mathematical techniques such as Singular Value…”
Aggregating Continuous Word Embeddings for Information Retrieval (2013, background): “This Vector Space Model (VSM) or bag-of-words (BoW) representation is at the root of topic models such as Latent Semantic In-dexing (LSI) (Deerwester, 1988), Probablistic Latent Semantic Analysis (PLSA) (Hofmann, 1999) or Latent Dirichlet Allocation (LDA) (Blei et al., 2003).”
Robust Computation of Linear Models by Convex Relaxation (2012, unknown): “Latent semantic indexing We can describe a large corpus of documents that concern a small number of topics using a low-dimensional linear model [23].”
Software traceability with topic modeling (2010, unknown): “To copy otherwise, to republish,topostonserversortoredistributetolists,requirespriorspeci.c permissionand/or a fee.”
A framework for analyzing semantic change of words across time (2014, methodology): “The last vector representation, LSA based representation, is based on Latent Semantic Analysis [6].”
Using Topic Models in Content-Based News Recommender Systems (2013, methodology): “, 2003) and compare the learned features to those found by Latent Semantic Indexing (Deerwester, 1988).”