By Karen Sparck Jones, John Tait

Karen Sp?rck Jones is likely one of the significant figures of twentieth century and early twenty first Century computing and data processing. Her principles have had a huge impact at the improvement of web se's. Her contribution has been well-known through awards from the average language processing, details retrieval and synthetic intelligence groups, together with being requested to offer the celebrated Grace Hopper lecture. She remains to be an lively and influential researcher. Her contribution to the clinical evaluate of the effectiveness of such computers has been really amazing. This publication celebrates the existence and paintings of Karen Sp?rck Jones in her 70th yr. It comprises fifteen new and unique chapters written through major overseas professionals reviewing the cutting-edge and her effect within the parts within which Karen Sp?rck Jones has been lively. even though she has a e-book checklist which matches again over 40 years, it really is transparent even the very early paintings reviewed within the ebook should be learn with revenue by way of these engaged on fresh advancements in info processing like bioinformatics and the semantic internet.

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Extra resources for Charting a new course: natural language processing and information retrieval : essays in honour of Karen Spärck Jones

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2. UNIFIED MODEL Robertson, Maron and Cooper (1982) attempted to unify Models I and II, the result we will call Model III (Maron,1984). From a theoretical point of view this was a very interesting piece of research butt it did not lead to any significant experimentation. It highlighted the differences between the two models. Model I was seen as grouping users together in order to compute a probability of relevance for a given document, whereas Model II groups document documents together in order to compute a probability of relevance for a given user.

This gave an answer to the question posed above. It provided the probability that if a user described his need in terms of request II, then he will find that the 23 John I. ), Charting a New Course: Natural Language Processing and Information Retrieval. Essays in Honour of Karen Spärck Jones. 23–38 © 2005 Springer. J. VAN RIJSBERGEN document in question satisfies that need or is relevant. They called these latter probabilities relevance numbers and proposedd that they be used to rank the documents resulting from a request.

2. UNIFIED MODEL Robertson, Maron and Cooper (1982) attempted to unify Models I and II, the result we will call Model III (Maron,1984). From a theoretical point of view this was a very interesting piece of research butt it did not lead to any significant experimentation. It highlighted the differences between the two models. Model I was seen as grouping users together in order to compute a probability of relevance for a given document, whereas Model II groups document documents together in order to compute a probability of relevance for a given user.

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