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However, the situation in building weighting model with multiple factors and their correlation are complicated and difficult.
The remainder of this paper is organized as follows: we give methodology that explains how to automatically extract keywords from a given electronic medical record in Section 2 and Section 3. We present the term weighting model in query reformation in Section 3. Section 4 illustrates the experimental evaluation, and its result is described in Section 5. Section 6 summarizes the conclusions.
The values represent the weightings of each annotation class defined in each weighting model.
Here however, we propose a simple but flexible weighting model based on several physical realities in the system.
In addition, a new comprehensive weighting model is constructed to determine the influence of experts under group decision-making environment.
We propose a new feature selection method inspired by the query expansion technique developed for probabilistic weighting model proposed by Harman [12].
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The tests were repeated with all available (fixed effect and random effects) weighting models.
Users are also free to specify their own weighting models.
The training and validation of weighting models is described in subsequent sections.
As a requirement for various weighting models, as expected, data were normalized and all weights were 0 1.
SuRFR is distributed with three pre-trained weighting models that utilise publicly available data for variant annotation.
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