Discusses the use of inverted files in information retrieval systems and proposes a document identifier reassignment method to reduce the average gap values in an inverted file. Highlights include the d-gap technique; document similarity; heuristic algorithms; file compression; and performance evaluation from a simulation environment. (LRW)

Discusses the inclusion of contextual information in indexing and retrieval systems to improve results and the ability to carry out text analysis by means of linguistic knowledge. Presents research that investigated whether discourse variables have an impact on information and retrieval and classification algorithms. (Author/LRW)

Proposes a new approach for classifying text documents into two disjoint classes. Highlights include a brief overview of document clustering; a data mining approach called the One Clause at a Time (OCAT) algorithm which is based on mathematical logic; vector space model (VSM); and comparing the OCAT to the VSM. (Author/LRW)

Discusses information retrieval systems and extracting appropriate keywords from documents and proposes an effective substring search method by extending a pattern matching machine for multi-keywords called delayed keyword extraction (DKE). Also proposes a construction algorithm of the retrieval structure for speeding up the substring search.…

Presents an efficient text categorization (or text classification) algorithm for document retrieval of natural language texts that generates bigrams (two-word phrases) and uses the information gain metric, combined with various frequency thresholds. Experimental results suggest that the bigrams can substantially raise the quality of feature sets.…

Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

Highlights semantic links and shows how the semantic content of hypertext links can be used for information retrieval. Discussion includes indexing and retrieval algorithms that exploit link content and node content; retrieval strategies exploiting semantic links, including conventional retrieval and constrained spreading activation techniques;…

Database Tomography (DT) is a system which includes algorithms for extracting multi-word phrase frequencies and performing phrase proximity analyses on any type of large textual database. DT was used to derive technical intelligence from a near-earth space (NES) database derived from the Science Citation Index and the Engineering Compendex.…

In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…

Presents a probabilistic technique to retrieve passages from texts having a large size or heterogeneous semantic content. Results of experiments comparing the probabilistic technique to one based on a text segmentation algorithm revealed that the passage size affects passage retrieval performance; text organization and query generality may have an…