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Table 4. Interactivity and reactivity in messages by frequent contributors (percentages)
1998
"... In PAGE 11: ... More than two thirds of the sample of messages were written by authors who appear in the sample only once or twice. Table4 displays interactivity and reac- tivity of messages by frequent contributors. Messages by the most frequent contributors (10 or more messages per author), as well as those by frequent contributors (4 to 9 messages per au- thor), are significantly more reactive than the norm.... ..."
Cited by 14
Table 2. Top Eight Groups of Three Organisms that Contain Most Frequent Connected Subgraphs and Interactions
2006
"... In PAGE 16: .... melanogaster, S. cerevisiae, and R. norvegicus. Mining of PPI networks enables not only identification of frequent subgraphs but also phylogenetic analysis of modularity. In Table2 , we list the top eight groups of three organisms based on their shared interactions and subgraphs. While these results may be biased by the varying availability of interaction data for different organisms, they illustrate characteristics of modular phylogeny consistent with sequence-level phylogenetics.... ..."
Cited by 3
Table 2- Top eight groups of three organisms that contain most frequent connected subgraphs and interactions.
2006
"... In PAGE 20: ...elanogaster, S. cerevisiae, and R. norvegicus. Mining of PPI networks enables not only identification of frequent subgraphs but also phylogenetic analysis of modularity. In Table2 , we list the top eight groups of three organisms based on their shared interactions and subgraphs. While these results may be biased by the varying availability of interaction data for different organisms, they illustrate characteristics of modular phylogeny consistent with sequence-level phylogenetics.... ..."
Cited by 3
Table 3: Basic semantic categories and the meeting corpus (Wermter amp; Weber, 1996b). Di erences occurred mainly for verbs, e.g., NEED-events are very frequent in the railway counter interactions while SUGGEST- events are frequent in the business meeting interactions. The semantic categories of the 44
1997
"... In PAGE 11: ... Here we will primarily focus on the semantic categories of the meeting corpus. The basic semantic categories for a word are shown in Table3 . At a higher level of abstraction, each word can belong to an abstract semantic category.... ..."
Cited by 17
Table 3: Basic semantic categories and the meeting corpus (Wermter amp; Weber, 1996b). Di erences occurred mainly for verbs, e.g., NEED-events are very frequent in the railway counter interactions while SUGGEST- events are frequent in the business meeting interactions. The semantic categories of the 44
1997
"... In PAGE 11: ... Here we will primarily focus on the semantic categories of the meeting corpus. The basic semantic categories for a word are shown in Table3 . At a higher level of abstraction, each word can belong to an abstract semantic category.... ..."
Cited by 17
Table 2: Domain interactions in predictions of protein interactions in Plasmodium. Predicting protein interactions by their highest scoring domain interaction in P. falciparum we find the following 20 most frequent domain interactions. N refers to the domain interactions occurrence in the predicted set, %sl depicts the percentage of self protein interactions, and E is the expectation value of the underlying domain interaction.
2006
Table 2. Most frequent protein domains in the interaction dataset. Frequency expressed as fraction of total occurrences of each domain. Prediction using the Protein Families Database (Pfam v 5.5; Bateman et al., 2000)
"... In PAGE 2: ...en Markov models (Pfam v. 5.5; http://www.pfam.wustl. edu/), we estimated that at least 1394 distinct domains are represented. Table2 lists the most frequent protein do- mains found in DIP, using a sequence E-value cutoff level of 1.0.... ..."
Table 1: Frequent Items
"... In PAGE 4: ...4 Rule Information A result of the above problems and the combinatory nature of interaction relations is the low information content or readability of the rule results. In a preliminary study of our data we found strong itemsets under a non-relational setting Table1 . When we applied a basic relational setting to the same dataset, these itemsets dominated the results in combinations corresponding to the interaction setting we worked with (see Table 4 in Section 5).... ..."
Table 2: Shifting the focus of interaction
"... In PAGE 7: ... Our analysis of a selection of instances of such patterns focussed primarily on the sequential organization of an interaction, its work-relevant context, and the orientation of participants with respect to the prototype. The left side of Table2 lists the most frequent foci of interaction grouped according to overall orientation, either toward the machine or toward E apos;s work. Shifts among these foci were often the result of inquiries directed from E to S/K or vice versa as shown on the right side of Table 2.... In PAGE 7: ... The left side of Table 2 lists the most frequent foci of interaction grouped according to overall orientation, either toward the machine or toward E apos;s work. Shifts among these foci were often the result of inquiries directed from E to S/K or vice versa as shown on the right side of Table2 . Suppose, for example, that the participants were focussed on the machine, say, navigating through the running prototype.... ..."
Table 7.5: Measures of cohesive ties and chain interaction the texts. vis a vis each other in coherence. quot; An additional conclusion from the data is that in texts, which were \deemed unquestionably coherent, the CTs consistently formed above 50% of the TTs. quot; Summary To summarize, the theory of Hasan 1. implies importance of chain interaction for text cohesiveness; 2. chains interaction is a frequent phenomena in cohesive texts, so it can be computed.
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