An ontology is a speci...cation of a conceptualization, a shared understanding of some domain of interest. The paper develops an algorithm that hierarchically groups words together...
This paper describes a new representation for the audio and visual information in a video signal. We reduce the dimensionality of the signals with singular-value decompositions (S...
In recent years, Latent Semantic Indexing (LSI) has been recognized as an effective tool for Information Retrieval in text documents. The level of "granularity" in LSI (...
Abstract. In the area of information retrieval, the dimension of document vectors plays an important role. Firstly, with higher dimensions index structures suffer the "curse o...
Abstract. Latent semantic indexing (LSI) is an application of numerical method called singular value decomposition (SVD), which discovers latent semantic in documents by creating c...
This paper describes our participation in the TREC Legal competition in 2008. Our first set of experiments involved the use of Latent Semantic Indexing (LSI) with a small number of...
April Kontostathis, Andrew Lilly, Raymond J. Spite...
Abstract – The method of latent semantic indexing (LSI) is well known for tackling the synonymy and polysemy problems in information retrieval. However, its performance can be ve...
Latent Semantic Indexing (LSI) has been shown to be effective in recovering from synonymy and polysemy in text retrieval applications. However, since LSI ignores class labels of t...
Sutanu Chakraborti, Rahman Mukras, Robert Lothian,...
Previously topic models such as PLSI (Probabilistic Latent Semantic Indexing) and LDA (Latent Dirichlet Allocation) were developed for modeling the contents of plain texts. Recent...
Topic modeling has been a key problem for document analysis. One of the canonical approaches for topic modeling is Probabilistic Latent Semantic Indexing, which maximizes the join...
Deng Cai, Qiaozhu Mei, Jiawei Han, Chengxiang Zhai