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Table 1: Four layer meta modeling structure. Order Description Example

in Computer Automated Multi-Paradigm Modeling in Control System Design
by Pieter J. Mosterman, Hans Vangheluwe 2000
"... In PAGE 5: ...allInstances- gt;size lt; 10. The outlined meta modeling approach leads to the four layer structure in Table1 [2]. The object data row represents the data generated from a particular model, e.... ..."
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TABLE 2. Categorized items from the first part of the model perception questionnaire.

in Computerized Molecular Modeling - The New Technology For Enhancing Model Perception Among Chemistry Educators And Learners
by Nitza Barnea, Yehudit J. DORI

Table 1: Illustrating Prompt Constrained Natural Language as a stepping stone from isolated word recognition to unconstrained natural language understanding.

in Prompt Constrained Natural Language - Evolving The Next Generation Of Telephony Services
by Stephen Marcus Deborah, Stephen M. Marcus, Deborah W. Brown, Y G. Goldberg, Max S. Schoeffler, William R. Wetzel, Richard R. Rosinski 1996
"... In PAGE 2: ... In this, specific prompts are designed to elicit responses from the user that current recognition technology is able to handle. Table1 gives an example of isolated word recognition, PCNL and unconstrained natural language determining essentially the same information from a speaker. This is, of course, only a static snapshot.... In PAGE 2: ... In addition, though extreme care should be taken in modeling automated systems after human dialogs, callers are often used to providing information in a sequential and constrained fashion, prompted by a human agent. Note that the final example in Table1 is something that a human agent would not be able to handle, not because of limitations in human speech recognition, but because of limitations in human memory. It is therefore not unreasonable to suppose that improvements in automatic speech recognition and language understanding will one day result in systems which are able to perform at least such routine tasks more rapidly and accurately than a human listener.... ..."
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Table 1. The 13 qualitative interval relations, their names introduced for temporal reasoning, natural language expressions for the spatial domain, one graphical realization, and ordering of startpoints and endpoints (adapted and augmented according to Allen 1983).

in The Cognitive Adequacy of Allen's Interval Calculus for . . .
by Markus Knauff
"... In PAGE 5: ... Further 213 relations can be obtained as unions of the base relations. The base relations are illustrated in Table1 . The original names from Allen (1983; first column in Table 1) reflect that they were introduced for temporal reasoning.... In PAGE 5: ...ntervals. Further 213 relations can be obtained as unions of the base relations. The base relations are illustrated in Table 1. The original names from Allen (1983; first column in Table1 ) reflect that they were introduced for temporal reasoning. In the spatial domain the intervals represent segments on a line or curve and the second column shows how the relations can be translated into natural language expressions for the spatial domain.... ..."

Table 3. Language model (LM) descriptions of user topics

in A user-centered approach to evaluating topic models
by Diane Kelly, O Diaz, Nicholas J. Belkin, James Allan 2004
"... In PAGE 7: ... These topic models are estimated by using all of the evaluated documents and serve not as a method to be evaluated but rather to qualitatively represent the language modeling technique. For example, Table3 displays the top ten distinguishing words Kelly, D.... ..."
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Table 1 Timing results for model building for several example natural language commands.

in Exploring Model Building for Natural Language Understanding
by Johan Bos 2003
"... In PAGE 12: ... Table1 shows performances of MACE and PARADOX for di erent linguis- tic phenomena, including simple inde nite noun phrases (13), quanti cation (14), plural descriptions (15), negation (16), and anaphora (17). The results show that the treatment of plurals is costly|this is not surprising, as extra axioms are triggered and a new entity in the model is required for modelling... In PAGE 13: ... Results exceeding a time limit of 30 seconds are marked with \{ quot;. See Table1 for further information on speci cations. Number of Time (seconds) Example Devices MACE MACE PARADOX Model Size (13) 3 3.... In PAGE 13: ...20 15 (17) 9 { { 12.60 17 Table1 also reveals that PARADOX outperforms MACE in most of the cases. The processing speech for nding a model for a particular problem can be highly reduced by supplying the expected domain size to the model builder, particularly in the case of MACE.... ..."
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Table 1. Classifications of studies by their ways of using/combining quantitative and qualitative approaches and respective labels used by various authors

in THE COMBINED USE OF QUALITATIVE AND QUANTITATIVE Methods in . . .
by Katrin Niglas 2004
"... In PAGE 11: ...In Table1 , I have compared the classifications of studies that different authors have proposed. ... In PAGE 12: ... What seems a little questionable, is their choice of labels for these categories. Therefore, in the first row of Table1 , I have proposed new labels and short definitions for the three broad categories of research designs. For better and more detailed descriptions of different possibilities of combining quantitative and qualitative approaches, this broad classification needs to be divided into subcategories.... ..."

Table 1: The qualitative successor rules

in Qualitative simulation
by Benjamin Kuipers 1986
"... In PAGE 4: ... In order to guarantee that all possible behaviors are predicted, we require first that all possible qualitative value transitions are predicted, and second, that combinations of qualitative values are only deleted when they are inconsistent. Table1 enumerates all transitions from each qualitative value description to its possible successors. The validity of this table follows directly from the Intermediate Value and Mean Value Theorems from elementary calculus.... ..."
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Table 2 Recognition results (%) without normalising the d normalising on the classification of natural objects objects

in FLIR Image Segmentation and Natural Object Classification
by Sameer Singh, Markos Markou, John Haddon
"... In PAGE 3: ... The following observations are made from the results shown here. ! In Table2 , the two nearest neighbour models display similar performances across the ten folds. When the number of nearest neighbours increases from 1 to 3, model-1 nearest neighbour classifier is the best performer.... ..."

Table 1. Descriptive texts used for language model adaptation.

in Language Model Adaptation Through Topic Decomposition And MDI Estimation
by Marcello Federico 2002
"... In PAGE 3: ...See Project WEB site http://pc-erato2.iei.pi.cnr.it/echo shown in Table1 . From the acoustic content point of view, the test files contain about 55 minutes of audio, which can be partitioned into 19 minutes of music, 30 minutes of speech with background... ..."
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