Computer programs that access significant amounts of text usually include code that manipulates the textual objects that comprise it. Such programs include electronic mail readers, typesetters and, in particular, full-text information retrieval systems. Such code is often unsatisfying in that access to textual objects is either efficient, or flexible, but not both. A programming language like Awk or Perl provides very general facilities for describing textual objects, but at the cost of rescanning the text for every textual object. At the other extreme, full-text information retrieval systems usually offer access to a very limited number of kinds of textual objects, but this access is very efficient. The system described in this paper is a programming tool for managing textual objects. It provides a great deal of flexibility, giving access to very complex document structure, with a large number of constituent kinds of textual objects. Further, it provides access to these objects very efficiently, both in terms of time and auxiliary space, by being very careful to access secondary storage only when absolutely necessary.

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. © 1990 John Wiley & Sons, Inc.

: The emergence of the CD-ROM as a storage medium for full-text databases raises the question of the maximum size database that can be contained by this medium. As an example, the problem of storing the Tr'esor de la Langue Fran¸caise on a CD-ROM is examined in this paper. The text alone of this database is 700 MB long, more than a CD-ROM can hold. But in addition the dictionary and concordance needed to access this data must be stored. A further constraint is that some of the material is copyrighted, and it is desirable that such material be difficult to decode except through software provided by the system. Pertinent approaches to compression of the various files are reviewed and the compression of the text is related to the problem of data encryption: specifically, it is shown that, under simple models of text generation, Huffman encoding produces a bit-string indistinguishible from a representation of coin flips. Categories and Subject Descriptors: E.3 E.4 H.3.2 J.5 General terms: ...

The emergence of the CD-ROM as a storage medium for full-text databases raises the question of the maximum size database that can be contained by this medium. As an example, the problem of storing the Trésor de la Langue Française on a CD-ROM is examined in this paper. The text alone of this database is 700 megabytes long, more than a CD-ROM can hold. In addition, the dictionary and concordance needed to access these data must be stored. A further constraint is that some of the material is copyrighted, and it is desirable that such material be difficult to decode except through software provided by the system. Pertinent approaches to compression of the various files are reviewed, and the compression of the text is related to the problem of data encryption: Specifically, it is shown that, under simple models of text generation, Huffman encoding produces a bit-string indistinguishable from a representation of coin flips.

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.

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 therefore the detection of relevant documents on the basis of terms found in queries. Singular-value decomposition is used to decompose a large term by document matrix into 50 to 150 orthogonal factors from which the original matrix can be approximated by linear combination; both documents and terms are represented as vectors in a 50- to 150- dimensional space. Queries are represented as pseudo-documents vectors formed from weighted combinations of terms, and documents are ordered by their similarity to the query. Initial tests find this automatic method very promising.