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Table 1: Summary of Tool Characteristics (* = fine-grained bounds checking on heap only)
"... In PAGE 4: ... Finally, a miss signifies that the program returned as if no overflow occurred. Table1 describes the versions of tools tested in our eval- uation. All tests were performed on a Red Hat Linux re- lease 9 (Shrike) system with dual 2.... ..."
Table 3. Simulation parameters for the multi-SMT system used to evaluate the fine-grain implementation of PCDM on emerg- ing microprocessors.
2005
"... In PAGE 7: ....3.3 Experimental Evaluation of Hardware Extensions We have used a multi-SMT simulator based on SimICS [11], to evaluate the impact of limited, realistic hardware support for thread execution and synchronization on the performance of the fine-grain implementation of PCDM. Table3 shows the parameters of our... ..."
Cited by 7
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 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 6: Overall results of the systems using Senseval-2 with respect fine-grained and coarse-grained senses fine coarse
"... In PAGE 6: ... As we can see, in bold there are some words that appear in the Topic Signatures for sense 3 obtained using Infomap showed in Table 4, where there are also a part of the Topic Signature for the other three senses. In Table6 appears a summary of the results of the indirect evaluation of Infomap and ExRetriever. This table presents the results for each type of query construction strategy (either A, B or C), each system (either Infomap or ExRetriever), and with several levels of sense granularity (either fine or coarse).... ..."
Table 7: F1 related to each POS with fine-grained Senseval Evaluation Method Query Noun Verb Adj
"... In PAGE 6: ... weights methods: using Infomap we obtain slightly better figures for occurrences while when using ExRetriever the best results appear for weights. In Table7 , we present the results per POS of the queries for each system. We can see that the best query for each POS always rely on A (monosemous strategy), the only difference is that sometimes the best result uses the occurrence or the weight measure method.... In PAGE 6: ... We can see that the best query for each POS always rely on A (monosemous strategy), the only difference is that sometimes the best result uses the occurrence or the weight measure method. In Table7 , we also include the results of the evaluation of the publicly available Topic Signatures acquired from the web. As there are only available the TS for the nom-... ..."
Table 1. Fine-grain hybrid parallelization
"... In PAGE 5: ... 4.2 Fine-grain parallelization The pseudo-code for the fine-grain hybrid parallelization is depicted in Table1 . The fine-grain hybrid implementation applies an OpenMP parallel work-sharing con- struct to the tile computation of the pure MPI code.... ..."
Table 1: Above: Several unigram Topics from 1.6M collection. Below: Several unigram/ngram topics from the 300k subset collection. Topics 98 and 156 from the larger collection are automatically split into 4 fine-grained topics (102,129,186,184) in the subset collection.
2006
"... In PAGE 3: ...6 million titles/abstracts of the Rexa corpus, producing 200 topics (in just 33 hours). A sample of the resulting topics can be seen in Table1 . In general, these topics were coarse and often spanned multiple discplines.... In PAGE 9: ...he WebBook and the Web Forager: An Information Workspace for ... Video (74) 7 Query by Image and Video Content: The QBIC System Semantic Web (186) 7 Resource Description Framework (RDF) Information Retrieval (45) 7 The Harvest Information Discovery and Access System Table1 0: Documents with highest topic transfer from Topic1 to any Topic2. speech synthesis (2) natural language (parsing) (16) hidden markov models (21) information retrieval (petri nets) (45) classification (50) information extraction (70) machine translation (96) digital libraries (102) documents (113) word sense disambiguation (118) speech recognition (120) web (129) search (132) information retrieval (138) machine learning (146) text (159) natural language (160) neural networks (173) data mining (176) context (178) web services (184) semantic web (186) collaborative filtering (188) Figure 3: A subset of the overall topic transfer network centered on Information Extraction and Digital Libraries .... ..."
Cited by 8
Table 1: Above: Several unigram Topics from 1.6M collection. Below: Several unigram/ngram topics from the 300k subset collection. Topics 98 and 156 from the larger collection are automatically split into 4 fine-grained topics (102,129,186,184) in the subset collection.
2006
"... In PAGE 3: ...6 million titles/abstracts of the Rexa corpus, producing 200 topics (in just 33 hours). A sample of the resulting topics can be seen in Table1 . In general, these topics were coarse and often spanned multiple discplines.... In PAGE 9: ...he WebBook and the Web Forager: An Information Workspace for ... Video (74) 7 Query by Image and Video Content: The QBIC System Ontologies (186) 7 Resource Description Framework (RDF) Information Retrieval (45) 7 The Harvest Information Discovery and Access System Table1 0: Documents with highest topic transfer from Topic1 to any Topic2. speech synthesis (2) natural language (parsing) (16) hidden markov models (21) information retrieval (petri nets) (45) classification (50) information extraction (70) machine translation (96) digital libraries (102) documents (113) word sense disambiguation (118) speech recognition (120) web (129) search (132) information retrieval (138) machine learning (146) text (159) natural language (160) neural networks (173) data mining (176) context (178) web services (184) semantic web (186) collaborative filtering (188) Figure 3: A subset of the overall topic transfer network centered on Information Extraction and Digital Libraries .... ..."
Cited by 8
Table 1: Above: Several unigram Topics from 1.6M collection. Below: Several unigram/ngram topics from the 300k subset collection. Topics 98 and 156 from the larger collection are automatically split into 4 fine-grained topics (102,129,186,184) in the subset collection.
"... In PAGE 3: ...6 million titles/abstracts of the Rexa corpus, producing 200 topics (in just 33 hours). A sample of the resulting topics can be seen in Table1 . In general, these topics were coarse and often spanned multiple discplines.... In PAGE 9: ...he WebBook and the Web Forager: An Information Workspace for ... Video (74) 7 Query by Image and Video Content: The QBIC System Ontologies (186) 7 Resource Description Framework (RDF) Information Retrieval (45) 7 The Harvest Information Discovery and Access System Table1 0: Documents with highest topic transfer from Topic1 to any Topic2. hidden markov models speech recognition word sense disambiguation information extraction natural language parsing text NLP digital libraries web and VR web pages IR and queries collaborative filtering autonomous agents facial expressions computer vision artificial intelligence ontologies data mining search engines IR and documents speech synthesis images web services machine translation video SVMs information retrieval Figure 3: A subset of the overall topic transfer network centered on Information Extraction and Digital Libraries.... ..."
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