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Table 2: Performance and short description of the supervised systems participating in the SENSEVAL-3 English lexical sample Word Sense Disambiguation task. Precision and recall figures are provided for both fine grained and coarse grained scoring. Corresponding team and reference to system description (in this volume) are indicated for the first system for each team.
2004
Cited by 21
Table 2: Performance and short description of the supervised systems participating in the SENSEVAL-3 English lexical sample Word Sense Disambiguation task. Precision and recall figures are provided for both fine grained and coarse grained scoring. Corresponding team and reference to system description (in this volume) are indicated for the first system for each team.
2004
Cited by 21
Table 3. Performance of the coarse-grain RIRS scheme relative to the fine-grain approach. Workload Performance improvement(%)
2006
"... In PAGE 9: ... In this section, we compare the effectiveness of the RIRS scheme at fine grain (100K cycles) and coarse grain (100M cycles) intervals. Table3 shows the performance of the coarse-grain scheme relative to the fine-grain approach for each work- load. While there are small differences from workload to workload, overall the coarse and fine-grain schemes achieve very similar performance.... ..."
Cited by 2
Table 3. Performance of the coarse-grain RIRS scheme relative to the fine-grain approach. Workload Performance improvement(%)
"... In PAGE 9: ... In this section, we compare the effectiveness of the RIRS scheme at fine grain (100K cycles) and coarse grain (100M cycles) intervals. Table3 shows the performance of the coarse-grain scheme relative to the fine-grain approach for each work- load. While there are small differences from workload to workload, overall the coarse and fine-grain schemes achieve very similar performance.... ..."
Table 9: Senseval-2 systems results for fine-grained and coarse-grained senses, in wining order only for nouns Method fine coarse
"... In PAGE 8: ... In that way, the sense distribution information is used to discard low-frequency senses. In Table9 , we also include into the comparison the Topic Signatures acquired from the web, now only considering the nouns of the test set. These results show that the web based TS rates between Infomap TS and ExRetriever TS systems.... ..."
Table 10: Senseval-2 systems results for fine-grained and coarse-grained senses, in wining order fine coarse
"... In PAGE 7: ... 4.2 Comparision with other SENSEVAL-2 systems In Table10 , we present the official results of the Senseval-2 of those systems declared to be unsupervised. When compar- ing with those systems, Infomap would score second while... ..."
Table 10. Results of fault type identification model obtained from the coarse-grained and fine-grained selected metric sets.
"... In PAGE 8: ... However, only the coarse-grained selected fault metrics were not enough for fault type identification, a fine- grained metric selection algorithm was proposed to fur- ther extract additional relevant metrics that affect the cor- responding fault type. Such preprocessing ground work establishes an effective filtering mechanism that permits higher accuracy of subsequent fault type identification as depicted in Table10 . The fault type predictive model ap- plying the coarse-grained selected metrics yields an aver- age of 85% and 82% accuracy on faulty classes and pre- dicted faulty classes, respectively.... ..."
Table 10. Results of fault type identification model obtained from the coarse-grained and fine-grained selected metric sets.
"... In PAGE 8: ... However, only the coarse-grained selected fault metrics were not enough for fault type identification, a fine- grained metric selection algorithm was proposed to fur- ther extract additional relevant metrics that affect the cor- responding fault type. Such preprocessing ground work establishes an effective filtering mechanism that permits higher accuracy of subsequent fault type identification as depicted in Table10 . The fault type predictive model ap- plying the coarse-grained selected metrics yields an aver- age of 85% and 82% accuracy on faulty classes and pre- dicted faulty classes, respectively.... ..."
Table 3. Results from Directed Message Control Field Injections in Chameleon All the detection techniques in Levels 1 and 2 were active, but the L1 detections were all by the coarse-grained signatures.
2000
"... In PAGE 14: ... Results from Directed Message Control Field Injections in Chameleon All the detection techniques in Levels 1 and 2 were active, but the L1 detections were all by the coarse-grained signatures. The results from Table3 show that the coarse-grained signature is effective in all but one case of mes- sage corruption. The important point is that in 35-60% of cases, the error is detected without a crash of the process.... ..."
Cited by 1
Table 3. Results from Directed Message Control Field Injections in Chameleon All the detection techniques in Levels 1 and 2 were active, but the L1 detections were all by the coarse-grained signatures.
"... In PAGE 14: ... Results from Directed Message Control Field Injections in Chameleon All the detection techniques in Levels 1 and 2 were active, but the L1 detections were all by the coarse-grained signatures. The results from Table3 show that the coarse-grained signature is effective in all but one case of mes- sage corruption. The important point is that in 35-60% of cases, the error is detected without a crash of the process.... ..."
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