The research in this dissertation consists of two parts: An active learning algorithm for hierarchical labels and an embedding-based retrieval algorithm. In the first part, we present a new approach for learning hierarchically decomposable concepts. The approach learns a high-level classifier (e.g., location vs. non-location) by separately…

Discussion of the failure of individual keywords to identify conceptual content of documents in retrieval systems highlights Metric Similarity Modeling, a method for creating vector space representation of documents based on modeling target interdocument similarity values. Semantic relatedness, latent semantic indexing, an indexing and retrieval…

Presents an improved inverted region quadtree variation called Dynamic Inverted Quadtree that is able to support and index a pictorial database. Pattern-searching algorithms are described, and the space needs of Fully Inverted and Dynamic Inverted Quadtrees are analytically compared based on a model of image randomness expressed as a branching…

Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

The six papers that comprise this document deal with topics related to the National Institute for Occupational Safety and Health (NIOSH) and/or its online document retrieval system, NIOSHTIC. Specific subjects include: (1) a method to catalog multiple sources of NIOSH publications for dissemination; (2) an input rejection algorithm used to provide…