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Table 6 General terms ontologies in publication domain

in Pervasive Computing Environment
by Kong Choi Yu, Kong Choi Yu 2004
"... In PAGE 7: ...able 5 General terms ontologies in time domain............................................................ 48 Table6 General terms ontologies in publication domain .... ..."

Table 5 General terms ontologies in time domain

in Pervasive Computing Environment
by Kong Choi Yu, Kong Choi Yu 2004
"... In PAGE 7: ...able 4 Mapping records in smart space monitor............................................................. 41 Table5 General terms ontologies in time domain.... ..."

Table 2: Generalization accuracies in terms of the percentage of correctly classified test instances

in Memory-Based Morphological Analysis
by Antal van den Bosch, Walter Daelemans

Table 2. Mapping of linguistic terms to generalized fuzzy numbers

in Quantitative Evaluation of Systems with Security Patterns Using a Fuzzy Approach
by Spyros T. Halkidis, Er Chatzigeorgiou, George Stephanides
"... In PAGE 6: ... We have chosen generalized fuzzy numbers instead of other existing approaches because the similarity measure for generalized fuzzy numbers has been proven to be robust in the cases where both crisp and fuzzy numbers are to be compared [9]. We used the mapping from linguistic terms to generalized fuzzy numbers shown in Table2... In PAGE 8: ... 2) We then perform the logical composition of values, according to rules for the gates of fault trees, starting from the values of primary events and ending at the computation of the risk for top event. 3) Finally we compare the risk for the top event computed in step 2, with the values in Table2 using the similarity metric from [9]. 4) The linguistic term with the highest similarity is chosen as the result.... ..."

Table 1: The first column contains patterns for general or common terms. The second column contains patterns for person profiles.

in Identifying Definitions in Text Collections for Question Answering. LREC
by Horacio Saggion 2004
Cited by 6

Table 1. The Top Level of the USAS System A. General and Abstract terms B. The Body and the Individual

in www.methodsnetwork.ac.uk Love – ‘a familiar or a devil’? An Exploration of Key Domains in Shakespeare’s Comedies and Tragedies
by unknown authors
"... In PAGE 2: ... The taxonomy employed in the (modern and historical) USAS system presently uses a hierarchy of twenty one major domains, expanding into 232 semantic field tags. Table1 shows the top level domains (see Appendix 1 for the full taxonomy): AHRC ICT Methods Network, Centre for Computing in the Humanities, Kay House, 7 Arundel Street, London, WC2R 3DX. ... ..."

Table 1. Precision and recall performance of the suffix and the WordNet heuristics for three general classes of terms: Actions, States, and Chemicals

in ORGANIZING THE WEB: SEMI-AUTOMATIC CONSTRUCTION OF A FACETED SCHEME
by Kiduk Yang, Elin K. Jacob, Aaron Loehrlein, Seungmin Lee, Ning Yu
"... In PAGE 4: ... Terms that share a common WordNet category hierarchy were subsequently grouped to form a potential candidate facet. The groups produced by the WordNet heuristic were generally higher in both precision and recall than the groups formed by the suffix heuristic (see Table1 ). Another advantage of the WordNet heuristic is that it allows the granularity of class meanings to be modified more easily than the suffix heuristic.... ..."

Table 13.6. Results obtained with the Perceptron in terms of the level of general- isation

in 13 Dimensionality Reduction and Microarray data
by David A. Elizondo, Benjamin N. Passow, Ralph Birkenhead, Andreas Huemer

Table 13 Most General Unifier M is a ground term, while N may be composed of variables.

in Dynamic types for authentication
by Michele Bugliesi, Riccardo Focardi, Matteo Maffei 2007
"... In PAGE 38: ...ecryption (cf. DECRYPT). ENCRYPT behaves as expected. Notice that the generation of both names (RES) and keys (SYMMETRIC and ASYMMETRIC KEY) are formal- ized as semantic transitions in which the freshly generated name/key is required to be different from all the already used names. Pattern-matching is formalized by the notion of most general unifier, defined in Table13 : the most general unifier takes as input a ground term and a term possibly containing variables and yields a substitution. Specifically, the most general unifier yields the empty substitution when applied to equal terms and the most general unifier between M and N when applied to TAG(M) and TAG(N), where the tag TAG is the same: the side condition M 6 = N makes the function m:g:u: deterministic.... ..."
Cited by 3

Table 1: Generalized quanti er operators expressed in terms of the quanti er operator b 1.

in Unnesting and Optimization Techniques for Extended-SQL Queries Containing Generalized Quantifiers
by Sudhir G. Rao
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