Describes the development of a structural model of document retrieval based on lexical-semantic relationships between index terms. An algorithm that uses tree-to-tree distance to measure structural closeness between a document and a query statement is explained, and the proposed model is compared to a vector retrieval model. (18 references) (LRW)

This issue contains nine articles that provide an overview of trends and research in parallel information retrieval. Topics discussed include network design for text searching; the Connection Machine System; PThomas, an adaptive information retrieval system on the Connection Machine; algorithms for document clustering; and system architecture for…

Reviews aspects of the relationship between machine learning and information retrieval. Highlights include learning programs that extend from knowledge-sparse learning to knowledge-rich learning; the role of the thesaurus; knowledge bases; artificial intelligence; weighting documents; work frequency; and merging classification structures. (78…

Describes a fast algorithm for comparing the lists of terms representing documents in automatic classification experiments. Complexity and running time for the algorithm are compared to other procedures, and a short algol-like routine is presented in the appendix. Eight references are included. (Author/BK)