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: 15. Juni 2019)
1Tell, B.: On MARC and natural text searching : a review of Pauline Cochrane's Thinking grafted onto a Swedish spy on library matters.
In: Cataloging and classification quarterly. 54(2016) no.1, S.87-99.
Inhalt: Vgl.: Tell, B.: On MARC and natural text searching: a review of Pauline Cochrane's inspirational thinking grafted onto a Swedish spy on library matters. In: Saving the time of the library user through subject access innovation: Papers in honor of Pauline Atherton Cochrane. Ed.: W.J. Wheeler. Urbana-Champaign, IL: Illinois University at Urbana-Champaign, Graduate School of Library and Information Science 2000. S.46-58. Vgl.: DOI: 10.1080/01639374.2015.1116359.
2Tell, B.: On MARC and natural text searching : a review of Pauline Cochrane's inspirational thinking grafted onto a Swedish spy on library matters.
In: Saving the time of the library user through subject access innovation: Papers in honor of Pauline Atherton Cochrane. Ed.: W.J. Wheeler. Urbana-Champaign, IL : Illinois University at Urbana-Champaign, Graduate School of Library and Information Science, 2000. S.46-58.
Abstract: The following discussion is in appreciation of the invaluable inspirations Pauline Cochrane, by her acumen and perspicacity, has implanted into my thinking regarding various applications of library and information science, especially those involving machine-readable records and subject categorization. It is indeed an honor for me at my age to be offered to contribute to Pauline's Festschrift when instead I should be concerned about my forthcoming obituary. In the following, I must give some Background to what formed my thinking before my involvement in the field and thus before I encountered Pauline.
3Bartell, B.T. ; Cottrell, G.W. ; Belew, R.K.: Optimizing similarity using multi-query relevance feedback.
In: Journal of the American Society for Information Science. 49(1998) no.8, S.742-761.
Abstract: We propose a novel method for automatically adjusting paprameters in ranked-output text retrieval systems to improve retrieval performance. A renaked-output text retrieval system implements a ranking function which orders documents, placing documents estimated to be more relevant to the user's query before less relevant ones. The systems adjusts its parameters to maximize the match between the systems's document ordering and a target ordering. The target ordering is typically given by user feedback on a set of sample queries, but is more generally any document preference relation. We demonstrate the utility of the approach by using it to estimate a similarity measure (scoring the relevance of documents to queries) in a vector space model of information retrieval. Experimental results using several collections indicate that the approach automatically finds a simimilarity measure which performs equivalently to or better that all 'classic' similarity measures studied. It also performs within 1% of an estimated optimal measure (found by exhaustive sampling of the similarity measures). The method is compared to two alternative methods: a perceptron learning rule motivated by Wong and Yao's (1990) Query Formulation method, and a Least Squared learning rule, motivated by Fuhr and Buckley's (1991) Probabilisitc Learning approach. Though both alternatives have useful characteristics, we demonstrate empirically that neither can be used to estimate the parameters of the optimal similarity measure
4Bartell, B.T. ; Cottrell, G.W. ; Belew, R.K.: Representing documents using an explicit model of their similarities.
In: Journal of the American Society for Information Science. 46(1995) no.4, S.254-271.
Abstract: Proposes a method for creating vector space representations of documents based on modelling target interdocument similariyt values. The target similarity values are assumed to capture semantic relationships, or associations, between the documents. The vector representations are chosen so that the inner product similarities between document vector pairs closely match their target interdocument similarities. The method is closely related to the Latent Semantic Indexing approach
Objekt: Latent Semantic Indexing