Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 03. März 2020)
1Kontostathis, A. ; Pottenger, W.M.: ¬A framework for understanding Latent Semantic Indexing (LSI) performance.
In: Information processing and management. 42(2006) no.1, S.56-73.
Abstract: In this paper we present a theoretical model for understanding the performance of Latent Semantic Indexing (LSI) search and retrieval application. Many models for understanding LSI have been proposed. Ours is the first to study the values produced by LSI in the term by dimension vectors. The framework presented here is based on term co-occurrence data. We show a strong correlation between second-order term co-occurrence and the values produced by the Singular Value Decomposition (SVD) algorithm that forms the foundation for LSI. We also present a mathematical proof that the SVD algorithm encapsulates term co-occurrence information.
Anmerkung: Beitrag innerhalb eines thematischen Schwerpunktes "Formal Methods for Information Retrieval"
Objekt: Latent Semantic Indexing
2Wu, T. ; Pottenger, W.M.: ¬A semi-supervised active learning algorithm for information extraction from textual data.
In: Journal of the American Society for Information Science and Technology. 56(2005) no.3, S.258-271.
Abstract: In this article we present a semi-supervised active learning algorithm for pattern discovery in information extraction from textual data. The patterns are reduced regular expressions composed of various characteristics of features useful in information extraction. Our major contribution is a semi-supervised learning algorithm that extracts information from a set of examples labeled as relevant or irrelevant to a given attribute. The approach is semi-supervised because it does not require precise labeling of the exact location of features in the training data. This significantly reduces the effort needed to develop a training set. An active learning algorithm is used to assist the semi-supervised learning algorithm to further reduce the training set development effort. The active learning algorithm is seeded with a Single positive example of a given attribute. The context of the seed is used to automatically identify candidates for additional positive examples of the given attribute. Candidate examples are manually pruned during the active learning phase, and our semi-supervised learning algorithm automatically discovers reduced regular expressions for each attribute. We have successfully applied this learning technique in the extraction of textual features from police incident reports, university crime reports, and patents. The performance of our algorithm compares favorably with competitive extraction systems being used in criminal justice information systems.
Anmerkung: Beitrag in einem Themenheft zu: 'Intelligence and security informatics'
Themenfeld: Data Mining