### Table 5 Probabilistic model parameters

2006

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### Table 8: Probabilistic model: Terminology

"... In PAGE 38: ... Notation P denote the path being currently considered. Table8 summarizes the notation. 1 1 1 0.... ..."

### Table 1. Comparison of observed compositions with both possibilistic, extended possibilistic and probabilistic models (all ps lt;.0005).

"... In PAGE 3: ...ramework (e.g., negative predicted values). In order to reinforce the case against Possibility Theory, those measures were withdrawn from computation of the agreement between data and the probabilistic model. Results show that the probabilistic and possibilistic models both fitted the data ( Table1... ..."

### Table 4: Parameter Setting for OKAPI Probabilistic Model

"... In PAGE 5: ... In order to define an quot;optimal quot; parameter setting for the BM25 model, we have to conduct a set of experiments based on the CACM and CISI test- collections [Savoy 1995]. The results are depicted in Table4 . However, in our current context, we have set our retrieval scheme according to the parameter values given by [Robertson et al.... ..."

### Table 1 Summary of the four probabilistic models

1994

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### Table 5. A decomposable probabilistic model is in-

in A Simple Approach to Building Ensembles of Naive Bayesian Classifiers for Word Sense Disambiguation

### Table 1: Results of the probabilistic model for two patients.

### Table 2: Deterministic vs. Probabilistic Models

### Table 1. Probabilistic User Model

2006

"... In PAGE 4: ... In these studies, users marked 85% of formula cells on average when testing and debugging spreadsheets, often placing check-marks on cells, and rarely placing a23-marks on cells. Of the cells that users marked, users in our earlier studies made mistakes according to the probabilities given in Table1 , so for our study, we simulated user behavior based on these probabilities. The bold numbers in Table 1 highlight false positive (check on incorrect value) and false negative (a23 on correct value) oracle mistakes.... ..."

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### Table 2: Cascaded Chunking model vs Probabilistic model

"... In PAGE 5: ... 5.2 Experimental Results The results for the new cascaded chunking model as well as for the previous probabilistic model based on SVMs (Kudo and Matsumoto, 2000) are summa- rized in Table2 . We cannot employ the experiments for the probabilistic model using large dataset, since the data size is too large for our current SVMs learn- ing program to terminate in a realistic time period.... In PAGE 5: ...3 Probabilistic model vs. Cascaded Chunking model As can be seen Table2 , the cascaded chunking model is more accurate, efficient and scalable than the probabilistic model. It is difficult to apply the probabilistic model to the large data set, since it takes no less than 336 hours (2 weeks) to carry out the experiments even with the standard data set, and SVMs require quadratic or more computational cost on the number of training examples.... ..."