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Table 1. Constructs of mobile use context
"... In PAGE 1: ... Especially in mobile computing, an awareness of the context surrounding the users and their mobile devices has been a primary concern [1]. In this aspect, therefore, we propose the constructs of mobile use context as shown in Table1 . We categorize mobile use context according to the following: external and internal context.... In PAGE 2: ... Monitoring diary is composed of two parts: specific context questions based on our constructs of mobile use context, and usability problem in that context. The degree of each construct was measured based on 7-point scale, 0 through 6, for example, undistracted(0) through very distracted(6) in auditory distraction (see Table1 ). Part of the usability problem was reported as open-ended.... In PAGE 2: ... Results from the monitoring study As we predicted, the results from monitoring study indicate that mobile Internet users are, indeed, involved in widely varying use contexts. Moreover, each construct of our context model (see Table1 ), such as the degree of social interaction, auditory distraction, timeliness of use and so on, varies according to changing use situations. Indoor vs.... ..."
Table 3: Diagnostic confusion matrix not using context
2000
Cited by 4
Table 2. Results of using Context for Gist Differentiation
Table 2. Results of using Context for Gist Differentiation
TABLE 3. Definition of Buttons and Uses of Context in the Prototype
Table 2. Results of using Context for Gist Differentiation
Table 3: Using Richer Context
"... In PAGE 6: ...3.4 Using Richer Context Table3 shows the effect of varying the richness of the information that the decision tree algorithm is allowed to use in estimating the POS and word prob- abilities. The second column uses the approxima- tions given in Equation 9 and 10.... ..."
Table 4: Using Larger Contexts
1997
"... In PAGE 4: ...17 Table 3: Effect of Composite Questions 4.4 Effect of Larger Context In Table4 , we look at the effect of the size of the context, and compare the results to a word-based backoff language model [12] built using the CMU toolkit[14]. For a bigram model, it has a perplexity of 29.... ..."
Cited by 11
Table 4: Using Larger Contexts
1997
"... In PAGE 4: ...17 Table 3: Effect of Composite Questions 4.4 Effect of Larger Context In Table4 , we look at the effect of the size of the context, and compare the results to a word-based backoff language model [12] built using the CMU toolkit[14]. For a bigram model, it has a perplexity of 29.... ..."
Cited by 11
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