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”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 or-thogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are re-turned. initial tests find this completely automatic method for retrieval to be promising.

This paper describes a new approach for dealing with the vocabulary problem in human-computer interaction. Most approaches to retrieving textual materials depend on a lexical match between words in users' requests and those in or assigned to database objects. Because of the tremendous diversity in the words people use to describe the same object, lexical matching methods are necessarily incomplete and imprecise [5]. The latent semantic indexing approach tries to overcome these problems by automatically organizing text objects into a semantic structure more appropriate for matching user requests. This is done by taking advantage of implicit higher-order structure in the association of terms with text objects. The particular technique used is singular-value decomposition, in which a large term by text-object matrix is decomposed into a set of about 50 to 150 orthogonal factors from which the original matrix can be approximated by linear combination. Terms and objects are represented by 50 to 150 dimensional vectors and matched against user queries in this “semantic” space. Initial tests find this completely automatic method widely applicable and a promising way to improve users' access to many kinds of textual materials, or to objects and services for which textual descriptions are available.

We have previously described an extension of the vector retrieval method called "Latent Semantic Indexing" (LSI) (Deerwester, et al., 1990; Dumais, et al., 1988; Furnas, et al., 1988). The LSI approach partially overcomes the problem of variability in human word choice by automatically organizing objects into a "semantic" structure more appropriate for information retrieval. This is done by modeling the implicit higher-order structure in the association of terms with objects. Initial tests find this completely automatic method to be a promising way to improve users' access to many kinds of textual materials or to objects for which textual descriptions are available. This paper describes some enhancements to the basic LSI method, including differential term weighting and relevance feedback. Appropriate term weighting improves performance by an average of 40%, and feedback based on 3 relevant documents improves performance by an average of 67%. September 1, 1992 D R A F T Dumais - 2 1....

My overall objective is to develop and evaluate ways of automatically incorporating hypertext links into pre-existing scholarly journal articles. I describe a rule-based approach for making three types of links (structural, definition, and semantic). Structural links are a way of making explicit some connections between parts of the text. Definition links connect the use of a term, defined elsewhere in the document, to that definition. Links that connect parts of text that discuss similar things are semantic links. I distinguish several types of semantic links. I use two information retrieval (IR) systems (Cornell's SMART system and Bellcore's Latent Semantic Indexing) to select links based on the content of the articles. I conducted an experiment to compare the performance of the links forged using these two systems. The effectiveness of the links (and the rules used to make them) is tested by people reading the hypertext versions for information under a time constraint. A within-subj...

Under the assumption of weak perspective, two views of the same planar object are related through an affine transformation. In this paper, weconsider the problem of training a simple neural network to learn to predict the parameters of the affine transformation. Although the proposed scheme has similarities with other neural network schemes, its practical advantages are more profound. First of all, the views used to train the neural network are not obtained by taking pictures of the object from different viewpoints. Instead, the training views are obtained by sampling the space of affine transformed views of the object. This space is constructed using a single view ofthe object. Fundamental to this procedure is a methodology, based on Singular Value Decomposition (SVD) and Interval Arithmetic (IA), for estimating the ranges of values that the parameters of affine transformation can assume. Second, the accuracy ofthe proposed scheme is very close to that of a traditional least squares approach with slightly better space and time requirements. A front-end stage to the neural network, based on Principal Components Analysis (PCA), shows to increase its noise tolerance dramatically and also to guides us in deciding how many training views are necessary in order for the network to learn a good, noise tolerant, mapping. The proposed approach has been tested using both artificial and real data.

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") in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. Initial tests find this completely automatic method for retrieval to be promising. Deerwester - 1 - 1.