This panel will employ two different interpretations of the phrase “growing up” to address areas of common interest between hypertext and information retrieval researchers. First, the panelists will question whether or not hypertext is “growing up” as a scientific discipline; They will discuss characteristics that separate hypertext research from other related disciplines. Second, the panelists will discuss the problems encountered when a hypertext system “grows up” in size and complexity; They will discuss the very real problems expected when representing and integrating large knowledge bases, accommodating multiple users, and distributing single logical hypertexts across multiple physical sites.

MICROARRAS is an advanced full-text retrieval and analysis system. It supports fast, efficient browsing of a document's vocabulary as well as its text, recursive analytic categories, Boolean search with flexible context specifications, evaluation of arithmetic expressions, and graphical display of various numeric distributions. The system is designed to work with large textbases stored on remote mainframes or on a local store for a micro-computer or workstation. The description covers system architecture, design principals, as well as user functions.

Abstract Full‐text systems that access text randomly cannot normally determine the format operations in effect for a given target location. The problem can be solved by viewing the format marks as the non‐terminals in a format grammar. A formatted text can then be parsed using the grammar to build a data structure that serves both as a parse tree and as a search tree. While processing a retrieved segment, a full‐text system can follow the search tree from root to leaf, collecting the format marks encountered at each node to derive the sequence of commands active for that segment. The approach also supports the notion of a ‘well formatted’ document and provides a means for verifying the well‐formedness of a given text. To illustrate the approach, a sample set of format marks and a sample grammar are given suitable for formatting and parsing the article as a sample text.

The problem of finding nearest neighbors to a query in a document collection is a special case of associative retrieval, in which searches are performed using more than one key. A nearest neighbors associative retrieval algorithm, suitable for document retrieval using similarity matching, is described. The basic structure used is a binary tree, at each node a set of keys (concepts) is tested to select the most promising branch. Backtracking to initially rejected branches is allowed and often necessary.