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Predicting Information Retrieval Performance

Af: Robert M. Losee Engelsk Paperback

Predicting Information Retrieval Performance

Af: Robert M. Losee Engelsk Paperback
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Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.

This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.

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Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively.

This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.

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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 59
ISBN-13: 9783031011894
Indbinding: Paperback
Udgave:
ISBN-10: 3031011899
Kategori: Netværkskomponenter
Udg. Dato: 19 dec 2018
Længde: 0mm
Bredde: 191mm
Højde: 235mm
Forlag: Springer International Publishing AG
Oplagsdato: 19 dec 2018
Forfatter(e): Robert M. Losee
Forfatter(e) Robert M. Losee

Kategori Netværkskomponenter

ISBN-13 9783031011894

Sprog Engelsk

Indbinding Paperback

Sider 59

Udgave

Længde 0mm

Bredde 191mm

Højde 235mm

Udg. Dato 19 dec 2018

Oplagsdato 19 dec 2018

Forlag Springer International Publishing AG

Kategori sammenhænge