"... In PAGE 4: ...exicalized using a count noun (e.g., meter ). 3.3 Results for mass-count distinction Table2 shows the results of 10-fold cross validation for the mass-count classification. This was produced using the J48 algorithm in the Weka machine learning package [Witten and OpenCyc Cyc Instances 2607 30676 Entropy 0.... In PAGE 5: ... In cases where several reference terms correspond to the same synset, the features will be conflated. Given the 256 reference terms used for the Cyc-based re- sults (shown in Table2 ), the process to establish correspon- dences yields 70 distinct features (due to 62 deletions and 124 conflations). Table 4 shows the results, indicating an ac- OpenCyc Cyc Instances 3395 30675 Entropy 0.... ..."

"... In PAGE 6: ... 4.1 Results for mass-count distinction Table3 shows the results for the special case mass-count classification. This shows that the system achieves an accuracy of 90.... In PAGE 7: ...6 Table 4: General speech part classification using Cyc. (See Table3 for legend.) OpenCyc Cyc # Instances 3395 30675 # Classes 2 2 Entropy 0.... In PAGE 7: ...3 Results using WordNet criteria To see how well the criteria can be utilized from non- Cyc applications, the reference terms were mapped into WordNet synsets, as described above. Given the 256 ref- erence terms used for the Cyc-based results (shown in Table3 ), the process to establish correspondences yields 70 distinct features (due to the deletion of 62 terms hav- ing no corresponding synset and the conflation of 124 terms corresponding to the same synset). Tables 5 and 6 shows the results, indicating an accuracy of 86.... ..."

"... In PAGE 5: ...Table 2: Nondistict elements The rst example (see Table1 ) consists of a list scaled to [18; 24] interval with distict (but tightly closed) items. Its order is: d3 gt; d2 gt; d5 gt; d4 gt; d1 and simulation results (MATLAB) show: 8 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; lt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; : 1 gt; v2; v3 gt; 1 quot; = 59 60 1 10 = S v5 gt; 1 + quot; = 59 60 59 60 = 1 + quot; gt; v4; v1 gt; 1: Thus (6) is checked.... ..."

"... In PAGE 19: ... When the problem of context dependence is better understood, this part of the formalism will need to be changed. Thus, a denotational distinction of a word w is a quadruple of components as follows: w: (frequency strength indirectness concept) The first part of Table2 gives some examples for the distinctions of Figures 7 and 8. 5.... In PAGE 20: ...i.e., a variable) to one of the concepts specified in the core denotation of peripheral concepts. The second part of Table2 gives an example. 5.... In PAGE 20: ... Thus, we represent a stylistic distinction as follows: w: (degree dimension) where degree can take a value of low, medium,orhigh (though more values could easily be added to increase the precision). The third part of Table2 gives two examples. 6.... ..."

"... In PAGE 8: ..., 2002) so that the average letter frequency of the words in the four groups was approximately the same. Table2 shows means and standard deviations of the word frequencies, context frequencies, and orthographic distinctiveness scores for words in these conditions. Two study lists and two test lists were constructed randomly and anew for each subject.... ..."

"... In PAGE 8: ... Although the reduction rate depends on the data set, this cost reduction capability of the distinctiveness- sensitive NN search should be advantageous to the interac- tive similarity retrieval systems that need quick response to users. Table1 shows the number of distinctive nearest neigh- bors found by the distinctiveness-sensitive NN search. Be-... In PAGE 9: ... We can see that similar images are successfully retrieved by the distinctiveness-sensitive NN search. The amazing result of Table1 is that we obtained only one distinctive nearest neighbor for 46,040 images. Since each query image is chosen from the images in the data set, the obtained distinctive nearest neighbor is the query it- self.... ..."

"... In PAGE 12: ...Table 1: Distinct phase meshes The problems are constructed by taking a set of 2D amp; 3D meshes, some regular grids and some with irregular (or unstructured) adjacencies and geometrically bisecting them so that one half is assigned to phase 1 and the other half to phase 2. Table1 gives a summary of the mesh sizes and classification, where CEBD represents the number of type 1 vertices and similarly for CEBE. These are possibly the simplest form of two-phase problems that one could imagine and provide a demonstration of the need for multiphase mesh partitioning.... ..."